Morphological Breast Cancer Subtyping by Weakly Supervised Neural Networks
Mar 16, 2021·,
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0 min read
Matthew Hanna
Matthew C. H. Lee
Alican Bozkurt
Ran A. Godrich
Adam Casson
Patricia Raciti
Jocelyn Sue
Julian Viret
Brandon Rothrock
Thomas J. Fuchs
Abstract
We present a weakly supervised deep learning approach for morphological subtyping of breast cancer in whole slide images. Using image-level labels, our neural network system classifies breast cancer subtypes without requiring expensive pixel-level annotations.
Type
Publication
In Laboratory Investigation 101(Suppl 1), 109–110 (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.