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Abstract:
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It has been a longstanding challenge in geometric morphometrics and medical imaging to infer the physical locations (or regions) of 3D shapes that are most associated with a given response variable (e.g. class labels) without needing common predefined landmarks across the shapes, computing correspondence maps between the shapes, or requiring the shapes to be diffeomorphic to each other. I will talk about SINATRA: the first machine learning pipeline for sub-image analysis which identifies physical shape features that explain most of the variation between two classes without the aforementioned requirements. Along the way we will learn some things about integral geometry and differential topology, Bayesian statistics, primate molars, and glioblastomas.
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