Learning to localize mutation in lung adenocarcinoma histopathology images

Abstract

Molecular profiling of cancers is necessary to identify the optimal therapeutic options for patients. However, these assays are time-and-resource-intensive to perform, and they cannot accurately capture mutational heterogeneity. Here, we present a novel approach to address these issues.

Publication
NeurIPS, 2020, LMRL Workshop
Sizhe Lester Li
Sizhe Lester Li
李思哲

My research interests span robot learning, vision, and physics simulation. Currently, I develop methods for robots to learn to interact with deformable objects with challenging dynamics.