Learning-based Medical Image Analysis

Over the last 7+ years, deep learning has been transforming medical imaging, from enhancing acquisition to maximizing downstream utility of scans. Much of this progress relies on supervised learning approaches with “black box” models. In this talk, I will show two examples of recent work from my group where we move beyond this traditional paradigm to develop tools designed for the unique considerations of medical imaging. In the first part, I will focus on a classic problem: multi-modal image registration. I will describe a novel architecture we developed, coined KeyMorph, that affords the user robustness, control, and interpretability. In the second part, I will discuss the problem of localizing and quantifying change in longitudinal imaging studies, another classic setting in medical applications. Our learning-based approach exploits the arrow of time to characterize temporal change in serial images of a given individual, in an efficient, scalable, and easily implementable manner.