A Deep Learning Algorithm for the Diagnosis and Gleason Grading of Whole Slide Images of Prostate Cancer Core Biopsies

Abstract

Deep learning algorithms have shown promising early results in the automated diagnosis and grading of prostate cancer. However, training such algorithms typically requires a large amount of manually annotated training data. Herein, we developed a weakly supervised, deep learning approach for the diagnosis and Gleason grading of whole-slide images of prostate core biopsies.

Publication
The Journal of Urology, 2020
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.