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Proceedings Paper

Discovering correspondences between molecular profiles and morphological features via deep learning (Conference Presentation)

Paper Abstract

Tumor cell populations in histopathology exhibit enormous heterogeneity in phenotypic traits such as uncontrolled cellular and microvascular proliferation, nuclear atypia, recurrence and therapy response. However, there is a limited quantitative understanding of how the molecular genotype correspond with the morphological phenotype in cancer. In this work, we develop a deep learning algorithm that learns to map molecular profiles to histopathological patterns. In our preliminary results, we are able to generate high-quality, realistic tissue samples, and demonstrate that by attenuating the mutation of status of few genes, we are able to guide the histopathology tissue image synthesis to exhibit different phenotypes.

Paper Details

Date Published: 17 March 2020
Proc. SPIE 11320, Medical Imaging 2020: Digital Pathology, 113200M (17 March 2020); doi: 10.1117/12.2549889
Show Author Affiliations
Richard Chen, Harvard Medical School (United States)
Brigham and Women's Hospital (United States)
Faisal Mahmood, Harvard Medical School (United States)
Brigham and Women's Hospital (United States)

Published in SPIE Proceedings Vol. 11320:
Medical Imaging 2020: Digital Pathology
John E. Tomaszewski; Aaron D. Ward, Editor(s)

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