AIMI Research Meeting: Rethink Robustness of Deep Learning Models for Medical Image Analysis - Yuyin Zhou, PhD

Abstract:

The medical AI system robustness is the key to achieving reliable and trustworthy real-world deployment. In this talk, I will first introduce what a robust medical AI system should look like and why this is a challenging problem. Next, I will discuss how to build robust medical AI systems from the perspectives of data, models, and learning and describe different proposed methodologies from these aspects. Finally, I will reveal how these approaches advance robustness under different population shifts, non-iid data distributions and against noisy labels.

About:

Dr. Yuyin Zhou (https://yuyinzhou.github.io/) is an Assistant Professor of Computer Science and Engineering at UC Santa Cruz. She received her Ph.D. from the Computer Science Department at Johns Hopkins University in 2020 and was a postdoctoral researcher at Stanford University from 2020 to 2021. Yuyin’s research interests span the fields of medical image computing, computer vision, and machine learning, especially the intersection of them. She has published many papers at top-tier conferences and journals including CVPR, ICCV, AAAI, TPAMI, TMI, MedIA, etc. Yuyin Zhou has led the ICML 2021 workshop on Interpretable Machine Learning in Healthcare, the ICCV 2021 workshop on Computer Vision for Automated Medical Diagnosis, and co-organized ML4H 2021, the 9th CVPR MCV workshop. She served as a senior program committee for IJCAI 2021 and AAAI 2022, an area chair for MICCAI 2022, CHIL 2022.