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(6/23/2022) Speaker: Dylan Slack

University of California, Irvine

Title
Exposing Shortcomings and Improving the Reliability of Machine Learning Explanations
Abstract
For domain experts to adopt machine learning (ML) models in high-stakes settings such as health care and law, they must understand and trust model predictions. As a result, researchers have proposed numerous ways to explain the predictions of complex ML models. However, these approaches suffer from several critical drawbacks, such as vulnerability to adversarial attacks, instability, inconsistency, and lack of guidance about accuracy and correctness. For practitioners to safely use explanations in the real world, it is vital to properly characterize the limitations of current techniques and develop improved explainability methods. This talk will describe the shortcomings of explanations and introduce current research demonstrating how they are vulnerable to adversarial attacks. I will also discuss promising solutions and present recent work on explanations that leverage uncertainty estimates to overcome several critical explanation shortcomings.
Bio
Dylan Slack is a Ph.D. candidate at UC Irvine advised by Sameer Singh and Hima Lakkaraju and associated with UCI NLP, CREATE, and the HPI Research Center. His research focuses on developing techniques that help researchers and practitioners build more robust, reliable, and trustworthy machine learning models. In the past, he has held research internships at GoogleAI and Amazon AWS and was previously an undergraduate at Haverford College advised by Sorelle Friedler where he researched fairness in machine learning.
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(6/16/2022) Speaker: SUMMER BREAK!

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(6/9/2022) Speaker: SUMMER BREAK!

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(6/2/2022) Speaker: Huaxiu Yao

Stanford University

Title
Actionable Machine Learning for Tackling Distribution Shift
Abstract
To deploy machine learning algorithms in real-world applications, we must pay attention to distribution shift. When the test distribution differs from the training distribution, there will be a substantial degradation in model performance. To tackle the distribution shift, in this talk, I will present two paradigms with some instantiations. Concretely, I will first discuss how to build machine learning models that are robust to two kinds of distribution shifts, including subpopulation shift and domain shift. I will then discuss how to effectively adapt the trained model to the test distribution with minimal labeled data. The remaining challenges and promising future research directions will also be discussed.
Bio
Huaxiu Yao is a Postdoctoral Scholar in Computer Science at Stanford University, working with Prof. Chelsea Finn. Currently, his research focuses on building machine learning models that are robust to distribution shifts. He is also passionate about applying these methods to solve real-world problems with limited data. He obtained his Ph.D. degree from Pennsylvania State University. The results of his work have been published in top-tier venues such as ICML, ICLR, NeurIPS. He organized the MetaLearn workshop at NeurIPS, the pre-training workshop at ICML, and he served as a tutorial speaker at KDD, IJCAI, and AAAI.
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(5/26/2022) Speaker: Laura Manduchi

ETH Zurich

Title
Incorporating domain knowledge in deep generative models for weakly supervised clustering with applications to survival data
Abstract
The ever-growing amount of data and the time cost associated with its labeling have made clustering a relevant task in machine learning. Yet, in many cases, a fully unsupervised clustering algorithm might naturally find a solution that is not consistent with the domain knowledge. Additionally, practitioners often have access to prior information about the types of clusters that are sought, and a principled method to guide the algorithm towards a desirable configuration is then needed. This talk will explore how to integrate domain knowledge, in the form of pairwise constraints and survival data, in deep generative models. Leveraging side information in biomedical datasets enables exploratory analysis of complex data types, resulting in medically meaningful findings.
Bio
Laura is a PhD student in Computer Science at the Institute of Machine Learning at ETH Zürich under the supervision of Julia Vogt and Gunnar Rätsch. She is a member of the Medical Data Science group and of the ETH AI Centre. Her research lies at the interplay between probabilistic modelling and deep learning, with a focus on representation learning, deep generative models, and clustering algorithms. She is particularly interested in incorporating domain knowledge in the form of constraints and probabilistic relations to obtain preferred representations of data that are robust to biases, with applications in medical imaging and X-ray astronomy.
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(5/19/2022) Speaker: Xiaoyuan Guo

Emory Univeristy

Title
Facilitating the Curation and Future Analysis of Annotated Medical Images Across Institutions
Abstract
Medical imaging plays a significant role in different clinical applications such as detection, monitoring, diagnosis, and treatment evaluations of various clinical conditions. Supervised deep learning approaches have been popular in solving medical image related tasks. However, training such models often require large amounts of annotated data as supervision, which is often unavailable in the medical area. Therefore, curation of annotated data is promising to create a large-scale dataset and contribute to the development of supervised learning. Nonetheless, directly sharing data is prohibited due to the patient concerns. Without exchanging data between internal and external data sources, we propose to apply unsupervised anomaly detectors on the internal dataset and learn the clean in-distribution (ID). Then we share the trained models with the externals and detect the class-wise shift data (aka. out-of-distribution (OOD) data) with the anomaly detectors. Higher anomaly scores indicate more difference the external data owe. We also suggest the quantification methods to measure the shiftness of detected data and the external dataset quality after removing the shift samples. Furthermore, we design a corresponding content-based medical image retrieval method that can balance both the intra- and inter-class variance for OOD-sensitive retrieval. The designed shift data identification pipeline can be used to help detect noisy and under-represented data automatically, accelerating the curation process. Meanwhile, the OOD-aware image retrieval is suitable for image annotation, querying and future analysis in external datasets.
Bio
Xiaoyuan Guo is a Computer Science PhD student at Emory University, working with Prof. Imon Banerjee, Prof. Hari Trivedi and Prof. Judy Wawira Gichoya. Her primary research interests are computer vision and medical image processing. She is experienced in medical image segmentation, out-of-distribution detection, image retrieval, unsupervised learning, etc.
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(5/12/2022) Speaker: Ramon Correa

Arizona State University

Title
A review of Fair AI model development for image classification and prediction
Abstract
Artificial Intelligence (AI) models have demonstrated expert-level performance in image-based recognition and diagnostic tasks, resulting in increased adoption and FDA approvals for clinical applications. The new challenge in AI is to understand the limitations of models to reduce potential harm. Particularly, unknown disparities based on demographic factors could encrypt currently existing inequalities worsening patient care for some groups. In this talk, we will discuss techniques to improve model fairness for medical imaging applications alongside their limitations.
Bio
Ramon Correa is a Ph.D. student in ASU’s Data Science, Analytics, and Engineering program. His research interest involves studying model debiasing techniques. Previously, he completed his undergraduate studies at Case Western Reserve University, majoring in Biomedical Engineering.
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(5/5/2022) Speaker: Nandita Bhaskhar

Stanford University

Title
Beyond Test Set Performance - Rethinking Generalization Strategies for Clinical Deployment
Abstract
Artificial Intelligence (AI) and Deep Learning (DL) have seen tremendous successes across various domains in medicine. However, most of these successes have been limited to academic research, with their performance validated on siloed datasets. Real world deployment of deep learning models in clinical practice are rare. In this talk, I will discuss several studies and papers in a journal club format that demonstrate various challenges and risks in directly deploying current day models to the clinic. I will then lead a discussion surrounding strategies and recommendations for developing an evaluation framework and monitoring system to make our models suitable for deployment.
Bio
Nandita Bhaskhar (see website) is a PhD student in the Department of Electrical Engineering at Stanford University advised by Daniel Rubin. She is broadly interested in developing machine learning methodology for medical applications. Her current research focuses on (i) building label-efficient models through observational supervision and self-supervision for leveraging unlabelled medical data and (ii) developing strategies for reliable model deployment by assessing, quantifying and enhancing model trust, robustness to distribution shifts, etc. Prior to Stanford, she received her B.Tech in Electronics Engineering from the Indian Institute of Information Technology, IIIT, with the highest honours.
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(4/28/2022) Speaker: Petar Stojanov

Broad Institute

Title
Domain Adaptation with Invariant Representation Learning - What Transformations to Learn?
Abstract
Unsupervised domain adaptation, as a prevalent transfer learning setting, spans many real-world applications. With the increasing representational power and applicability of neural networks, state-of-the-art domain adaptation methods make use of deep architectures to map the input features X to a latent representation Z that has the same marginal distribution across domains. This has been shown to be insufficient for generating optimal representation for classification, and to find conditionally invariant representations, usually strong assumptions are needed. We provide reasoning why when the supports of the source and target data from overlap, any map of X that is fixed across domains may not be suitable for domain adaptation via invariant features. Furthermore, we develop an efficient technique in which the optimal map from X to Z also takes domain-specific information as input, in addition to the features X. By using the property of minimal changes of causal mechanisms across domains, our model also takes into account the domain-specific information to ensure that the latent representation Z does not discard valuable information about Y . We demonstrate the efficacy of our method via synthetic and real-world data experiments. The code is available at https://github.com/DMIRLAB-Group/DSAN.
Bio
Petar (website) is a postdoctoral researcher at the Broad Institute of MIT and Harvard, where he is supervised by Prof. Gad Getz and Prof. Caroline Uhler. He received his PhD in Computer Science at Carnegie Mellon University, where he was fortunate to be advised by Prof. Jaime Carbonell and Prof. Kun Zhang. Prior to that, he was an associate computational biologist at the Getz Lab. His research interests span machine learning and computational biology. He is currently very interested in applying causal discovery methodology to improve genomic analysis of cancer mutation and single-cell RNA sequencing data with the goal of understanding relevant causal relationships in cancer progression. His doctoral research was in transfer learning and domain adaptation from the causal perspective, a field which he is still interested and active.
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(4/21/2022) Speaker: Albert Gu

Stanford University

Title
Efficiently Modeling Long Sequences with Structured State Spaces
Abstract
A central goal of sequence modeling is designing a single principled model that can address sequence data across a range of modalities and tasks, particularly on long-range dependencies. Although conventional models including RNNs, CNNs, and Transformers have specialized variants for capturing long dependencies, they still struggle to scale to very long sequences of 10000 or more steps. This talk introduces the Structured State Space sequence model (S4), a simple new model based on the fundamental state space representation $x*(t) = Ax(t) + Bu(t), y(t) = Cx(t) + Du(t)$. S4 combines elegant properties of state space models with the recent HiPPO theory of continuous-time memorization, resulting in a class of structured models that handles long-range dependencies mathematically and can be computed very efficiently. S4 achieves strong empirical results across a diverse range of established benchmarks, particularly for continuous signal data such as images, audio, and time series.
Bio
Albert Gu is a final year Ph.D. candidate in the Department of Computer Science at Stanford University, advised by Christopher Ré. His research broadly studies structured representations for advancing the capabilities of machine learning and deep learning models, with focuses on structured linear algebra, non-Euclidean representations, and theory of sequence models. Previously, he completed a B.S. in Mathematics and Computer Science at Carnegie Mellon University, and an internship at DeepMind in 2019.
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(4/14/2022) Speaker: Sabri Eyuboglu

Stanford University

Title
Discovering Systematic Errors with Domino
Abstract
Machine learning models that achieve high overall accuracy often make systematic errors on coherent slices of validation data. In this talk , I introduce Domino, a new approach for discovering these underperforming slices. I also discuss a new framework for quantitatively evaluating methods like Domino.
Bio
Sabri is a 2nd Year CS PhD Student in the Stanford Machine Learning Group co-advised by Chris Ré and James Zou. He’s broadly interested in methods that make machine learning systems more reliable in challenging applied settings. To that end, he’s recently been working on tools that help practitioners better understand the interaction between their models and their data.
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(4/7/2022) Speaker: No session this week -- Spring Break!

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(3/31/2022) Speaker: Max Lu

MIT & Harvard Medical School

Title
Weakly-supervised, large-scale computational pathology for diagnosis and prognosis
Abstract
In this talk, I will outline a general framework for developing interpretable diagnostic and prognostic machine learning models based on digitized histopathology slides. Our method does not require manual annotation of regions of interest and can be easily scaled to tens of thousands of samples. Examples of application range from cancer subtyping and prognosis to predicting the primary origins of metastatic tumors.
Bio
Max is a 1st year Computer Science PhD student at MIT advised by Dr. Faisal Mahmood, currently interested in computational pathology and spatial biology. He obtained his B.S. degree in biomedical engineering and applied math and statistics from Johns Hopkins University. Before starting his PhD, his research primarily focused on developing machine learning algorithms for large scale quantitative analysis of digital histopathology slides.
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(3/24/2022) Speaker: Karan Singhal

Google Research

Title
Generalization and Personalization in Federated Learning with Connections to Medical AI
Abstract
Karan will present two recent works - "What Do We Mean by Generalization in Federated Learning?" (to appear at ICLR 2022, paper) and "Federated Reconstruction- Partially Local Federated Learning" (presented at NeurIPS 2021, paper, blog post). He'll give an overview of federated learning, discuss how we might think about generalization when we have multiple local data distributions, and provide an example of a method that improves final generalization to new data distributions. Throughout the talk, he'll connect the works to medical AI by discussing generalization to unseen patients and hospitals, in both federated and standard centralized settings.
Bio
Karan leads a team of engineers and researchers at Google Research working on representation learning and federated learning, with applications in medical AI. He is broadly interested in developing and validating techniques that lead to wider adoption of AI that benefits people. Prior to joining Google, he received an MS and BS in Computer Science from Stanford University.
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(3/17/2022) Speaker: Mikhail Khodak

Carnegie Mellon University

Title
Federated Hyperparameter Tuning - Challenges, Baselines, and Connections to Weight-Sharing
Abstract
Tuning hyperparameters is a crucial but arduous part of the machine learning pipeline. Hyperparameter optimization is even more challenging in federated learning, where models are learned over a distributed network of heterogeneous devices; here, the need to keep data on device and perform local training makes it difficult to efficiently train and evaluate configurations. In this work, we investigate the problem of federated hyperparameter tuning. We first identify key challenges and show how standard approaches may be adapted to form baselines for the federated setting. Then, by making a novel connection to the neural architecture search technique of weight-sharing, we introduce a new method, FedEx, to accelerate federated hyperparameter tuning that is applicable to widely-used federated optimization methods such as FedAvg and recent variants. Theoretically, we show that a FedEx variant correctly tunes the on-device learning rate in the setting of online convex optimization across devices. Empirically, we show that FedEx can outperform natural baselines for federated hyperparameter tuning by several percentage points on the Shakespeare, FEMNIST, and CIFAR-10 benchmarks, obtaining higher accuracy using the same training budget.
Bio
Misha is a PhD student in computer science at Carnegie Mellon University advised by Nina Balcan and Ameet Talwalkar. His research focuses on foundations and applications of machine learning, in particular the theoretical and practical understanding of meta-learning and automation. He is a recipient of the Facebook PhD Fellowship and has spent time as an intern at Microsoft Research - New England, the Lawrence Livermore National Lab, and the Princeton Plasma Physics Lab. Previously, he received an AB in Mathematics and an MSE in Computer Science from Princeton University.
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(3/10/2022) Speaker: Bin Li

University of Wisconsin-Madison

Title
Weakly supervised tumor detection in whole slide image analysis
Abstract
Histopathology is one of the essential tools for disease assessment. In modern histopathology, whole slide imaging (WSI) has become a powerful and widely used tool to visualize tissue sections in disease diagnosis, medical education, and pathological research. The use of machine learning brings great opportunities to the automatic analysis of WSIs that could facilitate the pathologists’ workflow and more importantly, enable higher-order or large-scale correlations that are normally very challenging in standard histopathology practices, such as differential diagnosis of hard-cases and treatment response predictions. This talk will cover our recent work of approaching the fundamental problem of weakly supervised classification and tumor localization in gigapixel WSIs with a novel multiple instance learning (MIL) model leveraged by self-supervised learning, as well as discussing the emerging challenges and opportunities in computational histopathology.
Bio
Bin Li is a Ph.D. candidate in Biomedical Engineering at the University of Wisconsin-Madison. He is currently a research assistant in the Laboratory for Optical and Computational Instrumentation, mentored by Prof. Kevin Eliceiri. He develops computational methods to improve the understanding of the biological and pathological mechanisms of disease development and patient care based on multi-modal microscopic image analysis.
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(3/3/2022) Speaker: Siyi Tang

Stanford University

Title
Self-Supervised Graph Neural Networks for Improved Electroencephalographic Seizure Analysis
Abstract
Automated seizure detection and classification from electroencephalography (EEG) can greatly improve seizure diagnosis and treatment. In this talk, I will present our recent work on graph-based modeling for EEG-based seizure detection and classification. We model EEG signals using a graph neural network and develop two EEG graph structures that capture the natural geometry of EEG sensors or dynamic brain connectivity. We also propose a self-supervised pre-training strategy to further improve the model performance, particularly on rare seizure types. Lastly, we investigate model interpretability and propose quantitative metrics to measure the model’s ability to localize seizures. ICLR paper link
Bio
Siyi Tang is a PhD candidate in Electrical Engineering at Stanford University, advised by Prof. Daniel Rubin. Her research aims to leverage the structure in medical data to develop better medical machine learning models and enable novel scientific discovery. She is also interested in enhancing the human interpretability and clinical utility of medical machine learning algorithms. Prior to Stanford, Siyi received her Bachelor of Engineering Degree with Highest Distinction Honor from National University of Singapore.
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(2/24/2022) Speaker: Mike Wu

Stanford University

Title
Optimizing for Interpretability in Deep Neural Networks
Abstract
Deep models have advanced prediction in many domains, but their lack of interpretability remains a key barrier to their adoption in many real world applications. There exists a large body of work aiming to help humans understand these black box functions to varying levels of granularity – for example, through distillation, gradients, or adversarial examples. These methods however, all tackle interpretability as a separate process after training. In this talk, we explore a different approach and explicitly regularize deep models so that they are well-approximated by processes that humans can step through in little time. Applications will focus on medical prediction tasks for patients in critical care and with HIV.
Bio
Mike is a fifth year PhD student in Computer Science at Stanford University advised by Prof. Noah Goodman. His primary research interests are in deep generative models and unsupervised learning algorithms, often with applications to education and healthcare data. Mike’s research has been awarded two best paper awards at AAAI and Education Data Mining as well as featured in the New York Times. Prior to Stanford, Mike was a research engineer at Facebook’s applied ML group.
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(2/17/2022) Speaker: Weston Hughes

Stanford University

Title
Deep Learning Methods for Electrocardiograms and Echocardiograms
Abstract
In this talk, we will discuss two recently published deep learning methods we’ve developed at Stanford and UCSF for the understanding of ECG and echocardiogram data. First, we'll discuss the development and evaluation of a convolutional neural network for multi class ECG interpretation which outperforms cardiologists and currently used ECG algorithms. Second, we’ll discuss a computer vision system for evaluating a range of biomarkers from echocardiogram videos. In our discussion of both papers, we’ll emphasize different analyses aiming to explain and interpret the models in different ways.
Bio
Weston Hughes is a 3rd year PhD student in the Computer Science department at Stanford, co-advised by James Zou in Biomedical Data Science and Euan Ashley in Cardiology. His research focuses on applying deep learning and computer vision techniques to cardiovascular imaging data, including electrocardiograms, echocardiograms and cardiac MRIs. He is an NSF Graduate Research Fellow.
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(2/10/2022) Speaker: Enze Xie (TIME CHANGE - 4PM to 5PM PST)

University of Hong Kong

Title
SegFormer - Simple and Efficient Design for Semantic Segmentation with Transformers
Abstract
We present SegFormer, a simple, efficient yet powerful semantic segmentation framework which unifies Transformers with lightweight multilayer perceptron (MLP) decoders. SegFormer has two appealing features- 1) SegFormer comprises a novel hierarchically structured Transformer encoder which outputs multiscale features. It does not need positional encoding, thereby avoiding the interpolation of positional codes which leads to decreased performance when the testing resolution differs from training. 2) SegFormer avoids complex decoders. The proposed MLP decoder aggregates information from different layers, and thus combining both local attention and global attention to render powerful representations. We show that this simple and lightweight design is the key to efficient segmentation on Transformers. We scale our approach up to obtain a series of models from SegFormer-B0 to SegFormer-B5, reaching significantly better performance and efficiency than previous counterparts. For example, SegFormer-B4 achieves 50.3% mIoU on ADE20K with 64M parameters, being 5x smaller and 2.2% better than the previous best method. Our best model, SegFormer-B5, achieves 84.0% mIoU on Cityscapes validation set and shows excellent zero-shot robustness on Cityscapes-C. Code is available here.
Bio
Enze Xie is currently a PhD student in the Department of Computer Science, The University of Hong Kong. His research interest is computer vision in 2D and 3D. He has published 16 papers (including 10 first/co-first author) in top-tier conferences and journals such as TPAMI, NeurIPS, ICML and CVPR with 1400+ citations. His work PolarMask was selected as CVPR 2020 Top-10 Influential Papers. He was selected into NVIDIA Graduate Fellowship Finalist. He has won 1st place in Google OpenImages 2019 instance segmentation track.
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(2/3/2022) Speaker: Jeffrey Gu

Stanford University

Title
Towards Unsupervised Biomedical Image Segmentation using Hyperbolic Representations
Abstract
Segmentation models are extremely useful for biomedical image analysis, but training segmentation models often require large, labelled datasets that are difficult and costly to acquire. Unsupervised learning is a promising approach for training segmentation models that avoids the need to acquire labelled datasets, but is made difficult by the lack of high-quality supervisory signal from expert annotations. Using the observation that biomedical images often contain an inherent hierarchical structure, we augment a VAE with additional supervisory signal via a novel self-supervised hierarchical loss. To aid the learning of hierarchical structure, we learn hyperbolic representations instead of Euclidean representations. Hyperbolic representations have previously been employed in fields such as natural language processing (NLP) as a way to learn hierarchical and tree-like structures, making them a natural choice of representation. In this talk, I will discuss hyperbolic representations for biomedical imaging as well as our recent paper on the topic.
Bio
Jeffrey Gu is a 2nd year Ph.D. student in ICME at Stanford University advised by Serena Yeung. His research interests include representation learning, unsupervised learning, biomedical applications, and beyond. Prior to Stanford, Jeffrey completed his undergraduate studies at the California Institute of Technology, majoring in Mathematics.
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(1/27/2022) Speaker: Jason Jeong

Arizona State University

Title
Applications of Generative Adversarial Networks (GANs) in Medical Image Synthesis, Translation, and Augmentation
Abstract
Medical imaging is a source of crucial information in modern healthcare. Deep learning models have been developed for various modalities such as CT, MRI, Ultrasound, and PET for automatic or semi-automatic diagnosis or assessment of diseases. While deep learning models have been proven to be very powerful, training such models sufficiently requires large, well-annotated but expensive datasets. However, medical images, especially those containing diseases, are rare. While there are a variety of solutions to improve models with limited and imbalanced datasets, one solution is generating these rare images through generative adversarial networks (GANs). In this presentation, I will present a quick review on the use of GANs in medical imaging tasks, specifically classification and segmentation. Then I will present and discuss our recent work on using GANs for generating synthetic dual energy CT (sDECT) from single energy CT (SECT). Finally, some interesting challenges and possible future directions of GANs in medical imaging will be discussed.
Bio
Jiwoong Jason Jeong is a Ph.D. student in ASU’s Data Science, Analytics, and Engineering program. His research interest involves applying GANs into the medical workflow with a focus on solving medical data imbalance and scarcity. Previously, he completed his Master’s in Medical Physics at Georgia Institute of Technology.
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(1/20/2022) Speaker: Lequan Yu (TIME CHANGE - 4PM to 5PM PST)

University of Hong Kong

Title
Medical Image Analysis and Reconstruction with Data-efficient Learning
Abstract
Medical imaging is a critical step in modern healthcare procedures. Accurate interpretation of medical images, e.g., CT, MRI, Ultrasound, and histology images, plays an essential role in computer-aided diagnosis, assessment, and therapy. While deep learning provides an avenue to deliver automated medical image analysis and reconstruction via data-driven representation learning, the success is largely attributed to the massive datasets with abundant annotations. However, collecting and labeling such large-scaled dataset is prohibitively expensive and time-consuming. In this talk, I will present our recent works on building data-efficient learning systems for medical image analysis and reconstruction, such as computer-aided diagnosis, anatomical structure segmentation, and CT reconstruction. The proposed methods cover a wide range of deep learning and machine learning topics, including semi-supervised learning, multi-modality learning, multi-task learning, integrating domain knowledge, etc. The up-to-date progress and promising future directions will also be discussed.
Bio
Dr. Lequan Yu is an Assistant Professor at the Department of Statistics and Actuarial Science, the University of Hong Kong. Before joining HKU, he was a postdoctoral fellow at Stanford University. He obtained his Ph.D. degree from The Chinese University of Hong Kong in 2019 and Bachelor’s degree from Zhejiang University in 2015, both in Computer Science. He also experienced research internships in Nvidia and Siemens Healthineers. His research interests are developing advanced machine learning methods for biomedical data analysis, with a primary focus on medical images. He has won the CUHK Young Scholars Thesis Award 2019, Hong Kong Institute of Science Young Scientist Award shortlist in 2019, Best Paper Awards of Medical Image Analysis-MICCAI in 2017 and International Workshop on Machine Learning in Medical Imaging in 2017. He serves as the senior PC member of IJCAI, AAAI, and the reviewer for top-tier journals and conferences, such as Nature Machine Intelligence, IEEE-PAMI, IEEE-TMI, Medical Image Analysis, etc.
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(12/2/21 to 1/13/22) Speaker: Winter Break -- we will see you next year :)

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(11/25/21) Speaker: No session this week -- Thanksgiving break

Stanford University

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(11/18/21) Speaker: Ramon Correa

Emory University

Title
Adversarial debiasing with partial learning - medical image case-studies
Abstract
The use of artificial intelligence (AI) in healthcare has become a very active research area in the last few years. While significant progress has been made in image classification tasks, only a few AI methods are actually being deployed in hospitals. A major hurdle in actively using clinical AI models currently is the trustworthiness of these models. When scrutinized, these models reveal implicit biases during the decision making, such as detecting race, ethnic groups, and subpopulations. These biases result in poor model performance, or racial disparity, for patients in these minority groups. In our ongoing study, we develop a two-step adversarial debiasing approach with partial learning that can reduce the racial disparity while preserving the performance of the targeted task. The proposed methodology has been evaluated on two independent medical image case-studies - chest X-ray and mammograms, and showed promises in reducing racial disparity while preserving the performance.
Bio
Ramon Correa is a Ph.D. student in ASU’s Data Science, Analytics, and Engineering program. His research interest involves studying model debiasing techniques. Previously, he completed his undergraduate studies at Case Western Reserve University, majoring in Biomedical Engineering.
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(11/11/21) Speaker: Rocky Aikens

Stanford University

Title
Assignment Control Plots - A Visual Companion for Causal Inference Study Design
Abstract
An important step for any causal inference study design is understanding the distribution of the treated and untreated subjects in terms of measured baseline covariates. However, not all baseline variation is equally important. In the observational context, balancing on baseline variation summarized in a propensity score can help reduce bias due to self-selection. In both observational and experimental studies, controlling baseline variation associated with the expected outcomes can help increase the precision of causal effect estimates. We propose a set of visualizations that decompose the space of measured covariates into the different types of baseline variation important to the study design. These assignment-control plots and variations thereof visually illustrate core concepts of causal inference and suggest new directions for methodological research on study design. As a practical demonstration, we illustrate one application of assignment-control plots to a study of cardiothoracic surgery. While the family of visualization tools for studies of causality is relatively sparse, simple visual tools can be an asset to education, application, and methods development. (This work is in the peer-review process and is currently available as a preprint on arxiv https://arxiv.org/abs/2107.00122)
Bio
Rachael C. “Rocky” Aikens is a collaborative biostatistician. Her methodological research focuses on the development of simple, data-centered tools and frameworks to design stronger observational studies. As a collaborator, she has led the statistical analysis of randomized assessments of clinical decision support, clinical informatics applications in pediatrics, and lifestyle interventions for reducing sedentary behavior and improving nutrition. A central focus of her work is the design and deployment of simple, data-centered methodologies, grounded in the needs of applied researchers. She is finishing a doctoral degree in Biomedical Informatics at Stanford University with expected completion June 2022.
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(11/4/21) Speaker: Mars Huang (TIME CHANGE - 9AM to 10AM PST)

Stanford University

Title
Towards Generalist Medical Imaging AI Using Multimodal Self-supervised Learning
Abstract
In recent years, deep learning models have demonstrated superior diagnostic accuracy compared to human physicians in several medical domains and imaging modalities. While deep learning and computer vision provide promising solutions for automating medical image analysis, annotating medical imaging datasets requires domain expertise and is cost-prohibitive at scale. Therefore, the task of building effective medical imaging models is often hindered by the lack of large-scale manually labeled datasets. In a healthcare system where myriad opportunities and possibilities for automation exist, it is practically impossible to curate labeled datasets for all tasks, modalities, and outcomes for training supervised models. Therefore, it is important to develop strategies for training generalist medical AI models without the need for large-scale labeled datasets. In this talk, I will talk about how our group plan to develop generalist medical imaging models by combining multimodal fusion techniques with self-supervised learning.
Bio
Mars Huang is a 3rd year Ph.D. student in Biomedical Informatics at Stanford University, co-advised by Matthew P. Lungren and Serena Yeung. He is interested in combining self-supervised learning and multimodal fusion techniques for medical imaging applications. Previously, he completed his undergraduate studies at the University of California, San Diego, majoring in Computer Science and Bioinformatics.
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(10/28/21) Speaker: Sarah Hooper

Stanford University

Title
Training medical image segmentation models with less labeled data
Abstract
Segmentation is a powerful tool for quantitative analysis of medical images. Because manual segmentation can be tedious, be time consuming, and have high inter-observer variability, neural networks (NNs) are an appealing solution for automating the segmentation process. However, most approaches to training segmentation NNs rely on large, labeled training datasets that are costly to curate. In this work, we present a general semi-supervised method for training segmentation networks that reduces the required amount of labeled data. Instead, we rely on a small set of labeled data and a large set of unlabeled data for training. We evaluate our method on four cardiac magnetic resonance (CMR) segmentation targets and show that by using only 100 labeled training image slices---up to a 99.4% reduction of labeled data---the proposed model achieves within 1.10% of the Dice coefficient achieved by a network trained with over 16,000 labeled image slices. We use the segmentations predicted by our method to derive cardiac functional biomarkers and find strong agreement to expert measurements of predicted ejection fraction, end diastolic volume, end systolic volume, stroke volume, or left ventricular mass compared an expert annotator.
Bio
Sarah Hooper is a PhD candidate at Stanford University, where she works with Christopher Ré and Curtis Langlotz. She is broadly interested in applying machine learning to meet needs in healthcare, with a particular interest in applications that make quality healthcare more accessible. Sarah received her B.S. in Electrical Engineering at Rice University in 2017 and her M.S. in Electrical Engineering at Stanford University in 2020.
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(10/21/21) Speaker: Khaled Saab

Stanford University

Title
Observational Supervision for Medical Image Classification using Gaze Data
Abstract
Deep learning models have demonstrated favorable performance on many medical image classification tasks. However, they rely on expensive hand-labeled datasets that are time-consuming to create. In this work, we explore a new supervision source to training deep learning models by using gaze data that is passively and cheaply collected during clinical workflow. We focus on three medical imaging tasks, including classifying chest X-ray scans for pneumothorax and brain MRI slices for metastasis, two of which we curated gaze data for. The gaze data consists of a sequence of fixation locations on the image from an expert trying to identify an abnormality. Hence, the gaze data contains rich information about the image that can be used as a powerful supervision source. We first identify a set of gaze features and show that they indeed contain class discriminative information. Then, we propose two methods for incorporating gaze features into deep learning pipelines. When no task labels are available, we combine multiple gaze features to extract weak labels and use them as the sole source of supervision (Gaze-WS). When task labels are available, we propose to use the gaze features as auxiliary task labels in a multi-task learning framework (Gaze-MTL). You can find details in our MICCAI 2021 paper.
Bio
Khaled Saab is a PhD student at Stanford, co-advised by Daniel Rubin and Christopher Re. His research interests are in developing more sustainable and reliable ML models for healthcare applications. Khaled is a Stanford Interdisciplinary Graduate Fellow, one of the greatest honors Stanford gives to a doctoral student pursuing interdisciplinary research.
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(10/14/21) Speaker: Jean-Benoit Delbrouck

Stanford University

Title
Multimodal medical research at the intersection of vision and language
Abstract
Inspired by traditional machine learning on natural images and texts, new multimodal medical tasks are emerging. From Medical Visual Question Answering to Radiology Report Generation or Summarization using x-rays, we investigate how multimodal architectures and multimodal pre-training can help improving results.
Bio
Jean-Benoit holds a PhD in engineering science from Polytechnic Mons in Belgium and is now a postdoctoral scholar at the Departement of Biomedical Data Science . His doctoral thesis focused on multimodal learning on natural images and texts. His postdoctoral research focuses on applying new (or proven) methods on multimodal medical tasks at the intersection of vision and language.
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(10/7/21) Speaker: Jonathan Crabbé

University of Cambridge

Title
Explainable AI - from generalities to time series
Abstract
Modern machine learning models are complicated. They typically involve millions of operations to turn their input into a prediction. Hence, in a human perspective, they are complete black-boxes. When these models are used in critical areas such as medicine, finance and the criminal justice system, this lack of transparency appears as a major hindrance to their adoption. With the necessity to address this problem, the field of Explainable AI (XAI) thrived. In this talk, we will first illustrate how XAI allows to achieve a better understanding of these complex machine learning models in general. We will then focus on model for time series data, which constitutes a big portion of the medical data.
Bio
Jonathan Crabbé is a PhD student in the Department of Applied Mathematics from the University of Cambridge, he is supervised by Mihaela van der Schaar. He joins the van der Schaar lab following a MASt in in theoretical physics and applied mathematics at Cambridge, which he passed with distinction, receiving the Wolfson College Jennings Price.
Jonathan’s work focuses on the development of explainable artificial intelligence (XAI), which he believes to be one of the most interesting challenges in machine learning. He is particularly interested in understanding the structure of the latent representations learned by state of the art models. With his theoretical physics background, Jonathan is also enthusiastic about time series models and forecasting.
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(9/30/21) Speaker: Siyi Tang

Stanford University

Title
Graph-based modeling in computational pathology
Abstract
Advances in whole-slide imaging, deep learning, and computational power have enabled substantial growth in the field of computational pathology, including automating routine pathology workflows and discovery of novel biomarkers. Convolutional neural networks (CNNs) have been the most commonly used network architecture in computational pathology. However, a different line of work that leverages cellular interactions and spatial structures in whole slide images using graph-based modeling methods is emerging. In this journal club, I will lead a discussion on graph-based modeling, particularly graph neural networks, in the field of computational pathology.
Bio
Siyi Tang is a PhD candidate in Electrical Engineering at Stanford University, advised by Prof. Daniel Rubin. Her research aims to leverage the structure in medical data to develop better medical machine learning models and enable novel scientific discovery. She is also interested in enhancing the human interpretability and clinical utility of medical machine learning algorithms. Prior to Stanford, Siyi received her Bachelor of Engineering Degree with Highest Distinction Honor from National University of Singapore.
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(9/23/21) Speaker: Jared Dunnmon (TIME CHANGE - 2PM to 3PM PST)

Visiting Scholar, Stanford University

Title
The Many Faces of Weak Supervision in Medical Representation Learning - Harnessing Cross-Modality, Enabling Multi-task Learning, and Mitigating Hidden Stratification
Abstract
Weakly supervised machine learning models have shown substantial promise in unlocking the value of vast stores of medical information in support of clinical decisionmaking. In this talk, we will discuss several avenues by which these approaches can be used in real-world medical imaging applications, and the value that can be provided in each case. We first demonstrate how cross-modal weak supervision can be used to train models that achieve results statistically similar to those trained using hand labels, but with orders-of-magnitude less labeling effort. We then build on this idea to show how the large-scale multi-task learning made practical by weak supervision can provide value by supporting anatomically-resoved models for volumetric medical imaging applications. Finally, we discuss recent results indicating that weakly supervised distributionally robust optimization can be used to improve model robustness in an automated way.
Bio
Dr. Jared Dunnmon is currently a Visiting Scholar in the Department of Biomedical Data Science at Stanford University. Previously, Jared was an Intelligence Community Postdoctoral Fellow in Computer Science at Stanford, where he was advised by Profs. Chris Ré and Daniel Rubin. His research interests focus on combining heterogeneous data modalities, machine learning, and human domain expertise to inform and improve decisionmaking around such topics as human health, energy & environment, and geopolitical stability. Jared has also worked to bridge the gap between technological development and effective deployment in a variety of contexts including foreign policy at the U.S. Senate Foreign Relations Committee, solar electrification at Offgrid Electric, cybersecurity at the Center for Strategic and International Studies, emerging technology investment at Draper Fisher Jurvetson, nuclear fusion modeling at the Oxford Mathematical Institute, and nonlinear energy harvesting at Duke University. Jared holds a PhD from Stanford University (2017), a B.S. from Duke University, and both an MSc. in Mathematical Modeling and Scientific Computing and an M.B.A. from Oxford, where he studied as a Rhodes Scholar.
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(9/16/21) Speaker: No session this week -- summer break!

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(9/9/21) Speaker: No session this week -- summer break!

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(9/2/21) Speaker: Beliz Gunel

Stanford University

Title
Self-training vs. Weak Supervision using Untrained Neural Nets for MR Reconstruction
Abstract
Untrained neural networks use CNN architecture itself as an image prior for reconstructing natural images without requiring any supervised training data. This makes them a compelling tool for solving inverse problems such as denoising and MR reconstruction for which they achieve performance that is on-par with some state-of-the-art supervised methods. However, untrained neural networks require tens of minutes to reconstruct a single MR slice at inference time, making them impractical for clinical deployment. We propose using ConvDecoder to generate “weakly-labeled” data from undersampled MR scans at training time. Using few supervised pairs and constructed weakly supervised pairs, we train an unrolled neural network that gives strong reconstruction performance with fast inference time of few seconds. We show that our method considerably improves over supervised and self-training baselines in the limited data regime while mitigating the slow inference bottleneck of untrained neural networks. In this talk, I will also briefly talk about how self-training can be applied, and in fact be complementary to pre-training approaches, in other application domains such as natural language understanding.
Bio
Beliz Gunel is a fourth year PhD student in Electrical Engineering at Stanford University, advised by Professor John Pauly. Her research interests are primarily in representation learning for medical imaging and natural language processing, and building data-efficient machine learning methods that are robust to distribution drifts. She collaborates closely with Professor Akshay Chaudhari and Professor Shreyas Vasanawala, and had research internships at Google AI, Facebook AI, and Microsoft Research. Previously, she completed her undergraduate studies at University of California, Berkeley, majoring in Electrical Engineering and Computer Science.
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(8/26/21) Speaker: No session this week -- break!

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(8/19/21) Speaker: No session this week -- break!

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(8/12/21) Speaker: Juan Manuel Zambrano Chaves

Stanford University

Title
Multimodal opportunistic risk assessment for ischemic heart disease
Abstract
Current risk scores for predicting ischemic heart disease (IHD) risk—the leading cause of global mortality—have limited efficacy. While body composition (BC) imaging biomarkers derived from abdominopelvic computed tomography (CT) correlate with IHD risk, they are impractical to measure manually. Here, in a retrospective cohort of 8,197 contrast-enhanced abdominopelvic CT examinations undergoing up to 5 years of follow-up, we developed improved multimodal opportunistic risk assessment models for IHD by automatically extracting BC features from abdominal CT images and integrating these with features from each patient’s electronic medical record (EMR). Our predictive methods match and, in some cases, outperform clinical risk scores currently used in IHD risk assessment. We provide clinical interpretability of our model using a new method of determining tissue-level contributions from CT along with weightings of EMR features contributing to IHD risk. We conclude that such a multimodal approach, which automatically integrates BC biomarkers and EMR data can enhance IHD risk assessment and aid primary prevention efforts for IHD. In this talk, I will also go over other recent publications related to opportunistic imaging, body composition analysis and cardiovascular disease.
Bio
Juan Manuel pursuing is PhD in Biomedical Informatics at Stanford University, advised by Daniel Rubin and Akshay Chaudhari. He is broadly interested in developing informatics tools to aid clinicians in clinical practice. His current research focuses on multimodal data fusion, building models that leverage medical images in addition to other relevant data sources. He was previously awarded a medical degree in addition to a B.S. in biomedical engineering from Universidad de los Andes in Bogotá, Colombia.
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(8/5/21) Speaker: Mayee Chen

Stanford University

Title
Mandoline - Model Evaluation under Distribution Shift
Abstract
Machine learning models are often deployed in different settings than they were trained and validated on, posing a challenge to practitioners who wish to predict how well the deployed model will perform on a target distribution. If an unlabeled sample from the target distribution is available, along with a labeled sample from a possibly different source distribution, standard approaches such as importance weighting can be applied to estimate performance on the target. However, importance weighting struggles when the source and target distributions have non-overlapping support or are high-dimensional. Taking inspiration from fields such as epidemiology and polling, we develop Mandoline, a new evaluation framework that mitigates these issues. Our key insight is that practitioners may have prior knowledge about the ways in which the distribution shifts, which we can use to better guide the importance weighting procedure. Specifically, users write simple "slicing functions" - noisy, potentially correlated binary functions intended to capture possible axes of distribution shift - to compute reweighted performance estimates. We further describe a density ratio estimation framework for the slices and show how its estimation error scales with slice quality and dataset size. Empirical validation on NLP and vision tasks shows that Mandoline can estimate performance on the target distribution up to 3x more accurately compared to standard baselines.
This is joint work done with equal contribution from Karan Goel and Nimit Sohoni, as well as Fait Poms, Kayvon Fatahalian, and Christopher Ré. In this talk I will also connect the Mandoline framework to the broader theme of interactive ML systems and some of my collaborators research in this area.
Bio
Mayee Chen is a second year PhD student in Computer Science at Stanford University, advised by Professor Christopher Ré. She is interested in understanding the theoretical underpinnings of tools in modern machine learning and using them to develop new methods. Her current interests revolve around how to evaluate sources of supervision (e.g., weakly, semi-supervised, and self-supervised) throughout the ML pipeline, particularly through both information-theoretic and geometric lenses. Previously, she completed her undergraduate studies at Princeton University, majoring in Operations Research and Financial Engineering.
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(7/29/21) Speaker: Shantanu Thakoor

DeepMind

Title
Bootstrapped Self-Supervised Representation Learning in Graphs
Abstract
Self-supervised graph representation learning aims to construct meaningful representations of graph-structured data in the absence of labels. Current state-of-the-art methods are based on contrastive learning, and depend heavily on the construction of augmentations and negative examples. Achieving peak performance requires computation quadratic in the number of nodes, which can be prohibitively expensive. In this talk, we will present Bootstrapped Graph Latents (BGRL) a method for self-supervised graph representation learning that gets rid of this potentially quadratic bottleneck. We show that BGRL outperforms or matches previous methods on several established benchmark datasets, while consuming 2-10x less memory. Moreover, it enables the effective usage of more expressive GNN architectures, allowing us to further improve the state of the art. Finally, we will present our recent results on applying BGRL to the very large-scale data regime, in the OGB-LSC KDD Cup, where it was key to our entry being among the top 3 awardees our track.
Bio
Shantanu is a Research Engineer working at DeepMind. His primary research interests are in graph representation learning and reinforcement learning. Prior to this, he received his MS from Stanford University, where he was working on AI safety and neural network verification, and B.Tech. from IIT Bombay, where he worked on program synthesis. Recently, he has been interested in applying graph representation learning methods to large-scale problems, including the OGB Large Scale Challenge.
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(7/22/21) Speaker: Liangqiong Qu

Stanford University

Title
Rethinking Architecture Design for Tackling Data Heterogeneity in Federated Learning
Abstract
Federated learning is an emerging research paradigm enabling collaborative training of machine learning models among different organizations while keeping data private at each institution. Despite recent progress, there remain fundamental challenges such as lack of convergence and potential for catastrophic forgetting in federated learning across real-world heterogeneous devices. While most research efforts focus on improving the optimization process in FL, in this talk, we will provide a new perspective by rethinking the choice of architectures in federated models. We will show that simply replacing convolutional networks with Transformers can greatly reduce catastrophic forgetting of previous devices, accelerate convergence, and reach a better global model, especially when dealing with heterogeneous data. The code related to this talk is released here, to encourage future exploration in robust architectures as an alternative to current research efforts on the optimization front.
Bio
Liangqiong Qu is currently a postdoctoral researcher at Stanford University. She received her joint-PhD in Pattern Recognition and Intelligent System from University of Chinese Academy of Sciences (2017) and Computer Science from City University of Hong Kong (2017). Before Join Stanford, she was a postdoctoral researcher at IDEA lab in the University of North Carolina at Chapel Hill during 2018~2019. She has published over 20 peer-reviewed articles including top-tier venues such as CVPR, MedIA, TIP, and MICCAI, and she also wrote a book chapter in Big Data in Psychiatry and Neurology.
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(7/15/21) Speaker: No session this week -- Feedback Form here

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(7/8/21) Speaker: Andre Esteva

Salesforce Research

Title
Frontiers of Medical AI - Therapeutics and Workflows
Abstract
As the artificial intelligence and deep learning revolutions have swept over a number of industries, medicine has stood out as a prime area for beneficial innovation. The maturation of key areas of AI - computer vision, natural language processing, etc. - have led to their successive adoption in certain application areas of medicine. The field has seen thousands of researchers and companies begin pioneering new and creative ways of benefiting healthcare with AI. Here we'll discuss two vitally important areas - therapeutics, and workflows. In the space of therapeutics we'll discuss how multi-modal AI can support physicians in complex decision making for cancer treatments, and how natural language processing can be repurposed to create custom-generated proteins as potential therapeutics. Within workflows, we'll explore how to build a COVID-specialized search engine, and discuss ways in which this could empower health systems to securely, and accurately, search over their highly sensitive data.
Bio
Andre Esteva is a researcher and entrepreneur in deep learning and computer vision. He currently serves as Head of Medical AI at Salesforce Research. Notably, he has led research efforts in AI-enabled medical diagnostics, and therapeutic decision making. His work has shown that computer vision algorithms can match and exceed the performance of top physicians at diagnosing cancers from medical imagery. Expanded into video they can diagnose behavioral conditions like autism. In the space of AI-enabled therapeutics, his research leverages multi-modal datasets to train AI models that can personalize oncology treatments for patients by determing the best course of therapy for them. He has worked at Google Research, Sandia National Labs, and GE Healthcare, and has co-founded two tech startups. He obtained his PhD in Artificial Intelligence at Stanford, where he worked with Sebastian Thrun, Jeff Dean, Stephen Boyd, Fei-Fei Li, and Eric Topol.
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(7/1/21) Speaker: Rikiya Yamashita

Stanford University

Title
Learning domain-agnostic visual representation for computational pathology using medically-irrelevant style transfer augmentation
Abstract
Suboptimal generalization of machine learning models on unseen data is a key challenge which hampers the clinical applicability of such models to medical imaging. Although various methods such as domain adaptation and domain generalization have evolved to combat this challenge, learning robust and generalizable representations is core to medical image understanding, and continues to be a problem. Here, we propose STRAP (Style TRansfer Augmentation for histoPathology), a form of data augmentation based on random style transfer from non-medical style source such as artistic paintings, for learning domain-agnostic visual representations in computational pathology. Style transfer replaces the low-level texture content of an image with the uninformative style of randomly selected style source image, while preserving the original high-level semantic content. This improves robustness to domain shift and can be used as a simple yet powerful tool for learning domain-agnostic representations. We demonstrate that STRAP leads to state-of-the-art performance, particularly in the presence of domain shifts, on two particular classification tasks in computational pathology.
Bio
Rikiya Yamashita is radiologist turned applied research scientist working as a postdoctoral researcher in the Department of Biomedical Data Science at Stanford University. He is broadly interested in developing machine learning methodology for extracting knowledge from unstructured biomedical data. With his dual expertise, he is passionate about bridging the gap between machine learning and clinical medicine.
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(6/24/21) Speaker: Garrett Honke

Google X, the Moonshot Factory

Title
βVAE Representation Learning and Explainability for Psychopathology with EEG and the constraints on deployment in the real world
Abstract
Despite extensive standardization, diagnostic interviews for mental health disorders encompass substantial subjective judgment. Previous studies have demonstrated that EEG-based neural measures can function as reliable objective correlates of depression, or even predictors of depression and its course. However, their clinical utility has not been fully realized because of 1) the lack of automated ways to deal with the inherent noise associated with EEG data at scale, and 2) the lack of knowledge of which aspects of the EEG signal may be markers of a clinical disorder. Here we adapt an unsupervised pipeline from the recent deep representation learning literature to address these problems by 1) learning a disentangled representation using β-VAE to denoise the signal, and 2) extracting interpretable features associated with a sparse set of clinical labels using a Symbol-Concept Association Network (SCAN). We demonstrate that our method is able to outperform the canonical hand-engineered baseline classification method on a number of factors, including participant age and depression diagnosis. Furthermore, our method recovers a representation that can be used to automatically extract denoised Event Related Potentials (ERPs) from novel, single EEG trajectories, and supports fast supervised re-mapping to various clinical labels, allowing clinicians to re-use a single EEG representation regardless of updates to the standardized diagnostic system. Finally, single factors of the learned disentangled representations often correspond to meaningful markers of clinical factors, as automatically detected by SCAN, allowing for human interpretability and post-hoc expert analysis of the recommendations made by the model.
Bio
Garrett is a neuroscientist working as a Senior Research Scientist at X, the Moonshot Factory (formerly Google X). He works with projects in the early pipeline on problem areas that generally involve ML, datascience, and human-emitted data.
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(6/17/21) Speaker: Florian Dubost

Stanford University

Title
Hydranet -- Data Augmentation for Regression Neural Networks
Abstract
Deep learning techniques are often criticized to heavily depend on a large quantity of labeled data. This problem is even more challenging in medical image analysis where the annotator expertise is often scarce. We propose a novel data-augmentation method to regularize neural network regressors that learn from a single global label per image. The principle of the method is to create new samples by recombining existing ones. We demonstrate the performance of our algorithm on two tasks- estimation of the number of enlarged perivascular spaces in the basal ganglia, and estimation of white matter hyperintensities volume. We show that the proposed method improves the performance over more basic data augmentation. The proposed method reached an intraclass correlation coefficient between ground truth and network predictions of 0.73 on the first task and 0.84 on the second task, only using between 25 and 30 scans with a single global label per scan for training. With the same number of training scans, more conventional data augmentation methods could only reach intraclass correlation coefficients of 0.68 on the first task, and 0.79 on the second task.
Bio
Florian Dubost is a postdoctoral researcher in biomedical data science at Stanford University, CA, USA, and has with six years of experience in machine learning. He holds a PhD in medical computer vision and reached top rankings in international deep learning competitions. He is member of program committees at conference workshops in AI and medicine, authored a book in AI and neurology, and is an author and reviewer for top international journals and conferences in AI and medicine with over 20 published articles, including 11 as first author.
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(6/10/21) Speaker: Edward Choi

KAIST

Title
Learning the Structure of EHR with Graph Convolutional Transformer
Abstract
Large-scale electronic health records (EHR) provide a great opportunity for learning representation of clinical entities (such as codes, visits, patients). As EHR data are typically stored in a relational database, their diverse information (diagnosis, medications, etc) can be naturally viewed as a graph. In this talk, we will study how this graphical structure can be exploited, or even learned for supervised prediction tasks using a combination of graph convolution and self-attention. Additionally, we will briefly present more recent works regarding multi-modal learning using Transformers.
Bio
Edward Choi is currently an assistant professor at KAIST, South Korea. He received his PhD at Georgia Tech under the supervision of Professor Jimeng Sun, focusing on interpretable deep learning methods for longitudinal electronic health records. Before he joined KAIST, Ed was a software engineer at Google Health Research, developing deep learning models for predictive healthcare. His current research interests include machine learning, healthcare analytics, and natural language processing.
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(6/3/21) Speaker: No session this week -- summer break!

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(5/27/21) Speaker: Amara Tariq

Emory University

Title
Patient-specific COVID-19 Resource Utilization Prediction Using Fusion AI model
Abstract
Strain on healthcare resources brought forth by recent COVID-19 pandemic has highlighted the need for efficient resource planning and allocation through prediction of future consumption. Machine learning can predict resource utilization such as the need for hospitalization based on past medical data stored in electronic medical records (EMR). We experimented with fusion modeling to develop patient-specific clinical event prediction model based on patient’s medical history and current medical indicators. A review of feature importance provides insight for future research and feedback from the community on the significance of various predictors of COVID-19 disease trajectory.
Bio
Dr. Amara Tariq received her PhD degree in Computer Science from University of Central Florida in 2016 where she was Fulbright Scholar. Her research was focused on automatic understanding of cross-modal semantic relationships, especially relations between images and text. After earning her PhD, she designed and taught courses focused on Artificial Intelligence and Machine Learning at graduate and post-graduate level in her home country, i.e., Pakistan. Her research interests evolved to include multi-modal data related to the fields of bioinformatics and health science. Since the beginning of 2020, she has been working in post-doctoral research capacity at Bioinformatics department, Emory University, GA. At Emory University, her research has been focused on analyzing electronic medical records, imaging studies, and clinical reports and notes for intelligent decision making regarding disease management and healthcare resource optimization. Her research has resulted in publications in top-tier venues including IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), IEEE Transactions on Image Processing (TIP), IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), Journal of American College of Radiology (JACR), and npj Digital Medicine.
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(5/20/21) Speaker: Nandita Bhaskhar

Stanford University

Title
Self-supervision & Contrastive Frameworks -- a vision-based review
Abstract
Self-supervised representation learning and contrastive techniques have picked up a lot interest in the last couple of years, especially in computer vision. Until recently, deep learning's successes thus far have been associated with a supervised learning paradigm, wherein labelled datasets are used to train models on specific tasks. This need for labelled datasets has been identified as the bottleneck for scaling deep learning models across various tasks and domains. They rely heavily on costly, time-consuming dataset curation and labelling schemes.

Self-supervision allows us to learn representations from large unlabelled datasets. Instead of relying on labels for inputs, it depends on designing suitable pre-text tasks to generate pseudo-labels from the data directly. Contrastive learning refers to a special subset of these self-supervised methods that have achieved the most success recently. In this talk, I will go over the top 6 recent frameworks - SimCLR, MoCo V2, BYOL, SwAV, DINO and Barlow Twins, giving a deeper dive into their methodology & performance and comparing each of the frameworks' strengths and weaknesses and discuss their suitability for applications in the medical domain.
Bio
Nandita Bhaskhar (see website) is a PhD student in the Department of Electrical Engineering at Stanford University advised by Daniel Rubin. She received her B.Tech in Electronics Engineering from the Indian Institute of Information Technology, IIIT, with the highest honours. She is broadly interested in developing machine learning methodology for medical applications. Her current research focuses on observational supervision and self-supervision for leveraging unlabelled medical data and out-of-distribution detection for reliable clinical deployment. Outside of research, her curiosity lies in a wide gamut of things including but not restricted to biking, social dance, travelling, creative writing, music, getting lost, hiking and exploring new things.
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(5/13/21) Speaker: Xiaoyuan Guo

Emory University

Title
Segmentation and Quantification of Breast Arterial Calcifications (BAC) on Mammograms
Abstract
Measurements of breast arterial calcifications (BAC) can offer a personalized, noninvasive approach to risk-stratify women for cardiovascular disease such as heart attack and stroke. We aim to detect and segment breast arterial calcifications in mammograms accurately and suggest novel measurements to quantify detected BAC for future clinical applications. To separate BAC in mammograms, we propose a lightweight fine vessel segmentation method Simple Context U-Net (SCU-Net). To further quantify calcifications, we test five quantitative metrics to inspect the progression of BAC for subjects- Sum of Mask Probability Metric (PM), Sum of Mask Area Metric (AM), Sum of Mask Intensity Metric (SIM), Sum of Mask Area with Threshold Intensity Metric (TAMx) and Sum of Mask Intensity with Threshold X Metric (TSIMx). Finally, we demonstrate the ability of the metrics to longitudinally measure calcifications in a group of 26 subjects and evaluate our quantification metrics compared to calcified voxels and calcium mass on breast CT for 10 subjects.
Bio
Xiaoyuan Guo is a Computer Science PhD student at Emory University, working with Prof. Imon Banerjee, Prof. Hari Trivedi and Prof. Judy Wawira Gichoya. Her primary research interests are computer vision and medical image processing, especially improving medical image segmentation, classification, object detection accuracy with mainstream computer vision techniques. She is also interested in solving open-world medical tasks.
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(5/6/21) Speaker: Angshuman Paul

NIH

Title
Few-shot Chest X-ray Diagnosis Using Clinical Images and the Images from the Published Scientific Literature
Abstract
Few-shot learning is the art of machine learning that tries to mimic the human cognitive ability of understanding new object classes from a few labeled training examples. In the last few years, several few-shot learning methods have been proposed for different tasks related to natural images. However, few-shot learning is relatively unexplored in the field radiology image analysis. In this seminar, we will present two few-shot learning methods for chest x-ray diagnosis. Our first method uses a discriminative ensemble trained using labeled clinical chest x-ray images. The second method uses labeled chest x-ray images from the published scientific literature and unlabeled clinical chest x-ray images to train a machine learning model. Experiments show the superiority of the proposed methods over several existing few-shot learning methods.
Bio
Angshuman Paul (M.E., Ph.D.) is a visiting (postdoctoral) fellow at the National Institutes of Health, USA. His primary research interest is in Machine Learning, Medical Imaging, and Computer Vision. He earned his Ph.D. from the Indian Statistical Institute, India. He has held a visiting scientist position at the Indian statistical Institute (2019) and a graduate intern position at the University of Missouri-Columbia (2011). Dr. Paul is the recipient of the NIH Intramural Fellowship (2019) from the National Institutes of Health, USA, and the best paper award in the Tenth Indian Conference on Computer Vision, Graphics and Image Processing (ICVGIP, 2016). He serves as a reviewer of several journals including IEEE Transactions on Medical Imaging, Pattern Recognition Letters, and IEEE Transactions on Image Processing.
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(4/29/21) Speaker: Joseph Cohen

Stanford University

Title
Gifsplanation via Latent Shift - A Simple Autoencoder Approach to Counterfactual Generation for Chest X-rays
Abstract
Motivation Traditional image attribution methods struggle to satisfactorily explain predictions of neural networks. Prediction explanation is important, especially in medical imaging, for avoiding the unintended consequences of deploying AI systems when false positive predictions can impact patient care. Thus, there is a pressing need to develop improved models for model explainability and introspection.
Specific problem A new approach is to transform input images to increase or decrease features which cause the prediction. However, current approaches are difficult to implement as they are monolithic or rely on GANs. These hurdles prevent wide adoption.
Our approach Given an arbitrary classifier, we propose a simple autoencoder and gradient update (Latent Shift) that can transform the latent representation of a specific input image to exaggerate or curtail the features used for prediction. We use this method to study chest X-ray classifiers and evaluate their performance. We conduct a reader study with two radiologists assessing 240 chest X-ray predictions to identify which ones are false positives (half are) using traditional attribution maps or our proposed method. This work will be presented at MIDL 2021.
Results We found low overlap with ground truth pathology masks for models with reasonably high accuracy. However, the results from our reader study indicate that these models are generally looking at the correct features.We also found that the Latent Shift explanation allows a user to have more confidence in true positive predictions compared to traditional approaches (0.15±0.95 in a 5 point scale with p=0.01) with only a small increase in false positive predictions (0.04±1.06 with p=0.57).
Project Page https://mlmed.org/gifsplanation/
Source code https://github.com/mlmed/gifsplanation
Bio
Joseph Paul Cohen is a researcher and pragmatic engineer. He currently focuses on the challenges in deploying AI tools in medicine specifically computer vision and genomics and is affiliated to Stanford AIMI. He maintains many open source projects including Chester the AI radiology assistant, TorchXRayVision, and BlindTool – a mobile vision aid app. He is the director of the Institute for Reproducible Research, a US non-profit which operates ShortScience.org and Academic Torrents.
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(4/22/21) Speaker: Pradeeban Kathiravelu

Emory University

Title
Understanding Scanner Utilization with Real-Time DICOM Metadata Extraction
Abstract
Understanding system performance metrics ensures better utilization of the radiology resources with more targeted interventions. The images produced by radiology scanners typically follow the DICOM (Digital Imaging and Communications in Medicine) standard format. The DICOM images consist of textual metadata that can be used to calculate key timing parameters, such as the exact study durations and scanner utilization. However, hospital networks lack the resources and capabilities to extract the metadata from the images quickly and automatically compute the scanner utilization properties. Thus, they resort to using data records from the Radiology Information Systems (RIS). However, data acquired from RIS are prone to human errors, rendering many derived key performance metrics inadequate and inaccurate. Hence, there is motivation to establish a real-time image transfer from the Picture Archiving and Communication Systems (PACS) to receive the DICOM images from the scanners to research clusters to conduct such metadata processing to evaluate scanner utilization metrics efficiently and quickly.

In this talk, we present Niffler (https://github.com/Emory-HITI/Niffler), an open-source DICOM Framework for Machine Learning Pipelines and Processing Workflows. Niffler analyzes the scanners' utilization as a real-time monitoring framework that retrieves radiology images into a research cluster using the DICOM networking protocol and then extracts and processes the metadata from the images. Niffler facilitates a better understanding of scanner utilization across a vast healthcare network by observing properties such as study duration, the interval between the encounters, and the series count of studies. Benchmarks against using the RIS data indicate that our proposed framework based on real-time PACS data estimates the scanner utilization more accurately. Our framework has been running stable and supporting several machine learning workflows for more than two years on our extensive healthcare network in pseudo-real-time. We further present how we use the Niffler framework for real-time and on-demand execution of machine learning (ML) pipelines on radiology images.
Bio
Pradeeban Kathiravelu is a postdoctoral researcher at the Department of Biomedical Informatics in Emory University. He has an Erasmus Mundus Joint Doctorate in Distributed Computing from Universidade de Lisboa (Lisbon, Portugal) and Université catholique de Louvain (Louvain-la-Neuve, Belgium). His research focus includes researching and developing latency-aware Software-Defined Systems and cloud-assisted networks for radiology workflows at the edge.
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(4/15/21) Speaker: Jason Fries

Stanford University

Title
Weakly Supervised Learning in Medicine (Better Living through Programmatic Supervision)
Abstract
The high cost of building labeled training sets is one of the largest barriers to using supervised machine learning in medicine. Privacy concerns create additional challenges to sharing training data for modalities like patient notes, making it difficult to train state-of-the-art NLP tools for analyzing electronic health records. The COVID-19 pandemic underscores the need for faster, more systematic methods of curating and sharing training data. One promising approach is weakly supervised learning, where low cost and often noisy label sources are combined to programmatically generate labeled training data for commodity deep learning architectures such as BERT. Programmatic labeling takes a data-centric view of machine learning and provides many of the same practical benefits as software development, including better consistency, inspectability, and creating higher-level abstractions for experts to inject domain knowledge into machine learning models.

In this talk I outline our new framework for weakly supervised clinical entity recognition, Trove, which builds training data by combining multiple public medical ontologies and other imperfect label sources. Instead of manually labeling data, in Trove annotators focus on defining labelers using ontology-based properties like semantic types as well as optional task-specific rules. On four named entity benchmark tasks, Trove approaches the performance of models trained using hand-labeled data. However unlike hand-labeled data, our labelers can be shared and modified without compromising patient privacy.
Bio
Jason Fries (http://web.stanford.edu/~jfries/) is a Research Scientist at Stanford University working with Professor Nigam Shah at the Center for Biomedical Informatics Research. He previously completed his postdoc with Professors Chris Ré and Scott Delp as part of Stanford's Mobilize Center. He received his PhD in computer science from the University of Iowa, where he studied computational epidemiology and NLP methods for syndromic surveillance. His recent research explores weakly supervised and few-shot learning in medicine, with a focus on methods for incorporating domain knowledge into the training of machine learning models.
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(4/8/21) Speaker: Michael Zhang

Stanford University

Title
Federated Learning with FOMO for Personalized Training and Deployment
Abstract
Federated learning (FL) is an exciting and relatively new deep learning framework that canonically trains a single global model across decentralized local datasets maintained by participating clients. Accordingly with respect to making deep learning more deployable, FL is particularly promising in real world settings where technological or privacy constraints prevent individual data from being aggregated together. However, one model may not always be optimal for all participating clients. From healthcare to recommendation systems, we would ideally like to learn and deliver a personalized model for each participating client, as data may not be identically distributed from one client to another. This problem is emphasized when we consider how we might deploy FL in practice, where individual clients may only choose to federate if they can guarantee a benefit from the model produced at the end.

In this talk, I will present some recent work on one solution, called FedFomo, where each client effectively only federates with other relevant clients to obtain stronger personalization. First we will review federated learning as a machine learning framework, emphasizing the motivations behind personalized FL. I will then go into the origin story of FedFomo's name, highlighting a simple yet effective approach based both on the "fear of missing out" and "first order model optimization". In tandem, these ideas describe how FedFomo can efficiently figure out how much each client can benefit from another's locally trained model, and then use these values to calculate optimal federated models for each client. Critically, this does not assume knowledge of any underlying data distributions or client similarities, as this information is often not known apriori. Finally, I will describe recent empirical results on FedFomo's promising performance on a variety of federated settings, datasets, and degrees of local data heterogeneity, leading to wider discussion on the future directions and impact of federated learning and distributed machine learning, when personalization is in the picture.
Bio
Michael Zhang is a Computer Science PhD Student at Stanford, currently working with Chris Ré and Chelsea Finn. He is broadly interested in making machine learning more deployable and reliable in the "real world", especially through the lenses of improving model robustness and personalization to distribution shifts and new tasks, as well as developing new systems that enable collaborative machine learning and/or learning with less labels.
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(4/1/21) Speaker: Amirata Ghorbani

Stanford University

Title
Equitable Valuation of Data
Abstract
As data becomes the fuel driving technological and economic growth, a fundamental challenge is how to quantify the value of data in algorithmic predictions and decisions. For example, in healthcare and consumer markets, it has been suggested that individuals should be compensated for the data that they generate, but it is not clear what is an equitable valuation for individual data. In this talk, we discuss a principled framework to address data valuation in the context of supervised machine learning. Given a learning algorithm trained on a number of data points to produce a predictor, we propose data Shapley as a metric to quantify the value of each training datum to the predictor performance. Data Shapley value uniquely satisfies several natural properties of equitable data valuation. We introduce Monte Carlo and gradient-based methods to efficiently estimate data Shapley values in practical settings where complex learning algorithms, including neural networks, are trained on large datasets. We then briefly discuss the notion of distributional Shapley, where the value of a point is defined in the context of underlying data distribution.
Bio
Amirata Ghorbani is a fifth year PhD student at Stanford University advised by James Zou. He primarily works on different problems in machine learning such as research on equitable methods for data valuation, algorithms to interpret machine learning models, ways to make existing ML predictors fairer, and creating ML systems for healthcare applications such as cardiology and dermatology. He has also worked as a research intern in Google Brain, Google Brain Medical, and Salesforce Research.
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