Morphological Subtyping of Breast Cancer Using Machine Learning
Nov 1, 2021·,
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0 min read
Matthew Hanna
Matthew C. H. Lee
Alican Bozkurt
Peter Hamilton
Ran A. Godrich
Adam Casson
Patricia Raciti
Julian Viret
Brandon Rothrock
Thomas J. Fuchs
Abstract
We present a weakly supervised machine learning approach for morphological subtyping of breast cancer in whole slide images. Our system classifies invasive breast carcinomas into histological subtypes using deep neural networks trained on image-level labels.
Type
Publication
In The Journal of Pathology 255(Suppl 1), S35–S35 (2021)
Authors
AI Scientist
I am an AI Scientist at Paige AI. I did my Ph.D. with Jennifer Dy, Dana Brooks, and Jan-Willem van de Meent at Northeastern University. My main research interests are machine learning with emphasis on probabilistic programming, deep neural networks, and their applications in biomedical image processing. I am one of the developers of Probabilistic Torch, a library for deep generative models that extends PyTorch. I am also one of the maintainers of the PyTorch distributions module.