Abstract PD11-02: Subtyping Invasive Carcinomas and High-Risk Lesions for Machine Learning Based Breast Pathology
Feb 15, 2022·,
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Matthew G. Hanna
Patricia Raciti
Alican Bozkurt
Ran A. Godrich
Julian Viret
Donghun Lee
Philippe Mathieu
Matthew C. H. Lee
Brandon Rothrock
Thomas J. Fuchs
Abstract
We present a machine learning approach for subtyping invasive carcinomas and high-risk lesions in breast pathology whole slide images. Our system leverages deep learning to classify morphological subtypes, enabling automated and reproducible analysis of breast pathology specimens.
Type
Publication
In Cancer Research 82(4 Supplement), PD11-02 (2022)
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.