Link Search Menu Expand Document

Upcoming Talks


(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.
Video
Questions for the Speaker
Please add your questions to the speaker either to this google form or directly under the YouTube video

(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.
Video
Questions for the Speaker
Please add your questions to the speaker either to this google form or directly under the YouTube video

(5/26/2022) Speaker: Laura Manduchi

ETH Zurich

Video

(6/2/2022) Speaker: Huaxiu Yao

Stanford University

Video

(6/23/2022) Speaker: Dylan Slack

University of California, Irvine

Video

(6/30/2022) Speaker: Shibani Santurkar

Stanford University

Video

This site uses Just the Docs, a documentation theme for Jekyll. Source code for this version can be found here