Bias in Medical Studies in the Age of Large-Scale Models
Ehsan Adeli
February 22, 2023, Wednesday, 3:00 PM - 4:00 PM EST
The presence of confounding effects is inarguably one of the most critical challenges in medical applications. They influence both input (e.g., neuroimages) and the output (e.g., diagnosis or clinical score) variables and may cause spurious associations when not properly controlled for. Confounding effect removal is particularly difficult for a wide range of state-of-the-art prediction models, including deep learning methods. These methods operate directly on images and extract features in an end-to-end manner. This prohibits removing confounding effects by traditional statistical analysis, which often requires precomputed features (image measurements). In this talk, I will present methods to learn confounder-invariant discriminative features and novel normalization techniques to remove confounding and bias effects while training neural networks
Dr. Ehsan Adeli is an Assistant Professor (Clinical Educator Line) at Stanford University, School of Medicine, Department of Psychiatry and Behavioral Sciences, Computational Neuroscience (CNS) Lab. He is also affiliated with the Computer Science Department, Stanford AI Lab (SAIL), Stanford Vision and Learning (SVL) and the Stanford Partnership in AI-Assisted Care (PAC). HE is an Executive Co-Director of Stanford AGILE (Advancing technoloGy for fraIlty and LongEvity) Consortium funded by the Gordon and Betty Moore Foundation.

His research lies at the intersection of machine learning, computer vision, healthcare, and computational neuroscience. He works on automatic analysis of human activities and behaviors from videos and connecting how humans perform actions to the brain by analyzing magnetic resonance images (MRIs). He explores explainable machine learning algorithms for understanding the underlying factors of neurodegenerative and neuropsychiatric diseases on the brain as well as their ramifications for everyday life.