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17 March 2020 Discovering correspondences between molecular profiles and morphological features via deep learning (Conference Presentation)
Richard Chen, Faisal Mahmood
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Proceedings Volume 11320, Medical Imaging 2020: Digital Pathology; 113200M (2020) https://doi.org/10.1117/12.2549889
Event: SPIE Medical Imaging, 2020, Houston, Texas, United States
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.
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Richard Chen and Faisal Mahmood "Discovering correspondences between molecular profiles and morphological features via deep learning (Conference Presentation)", Proc. SPIE 11320, Medical Imaging 2020: Digital Pathology, 113200M (17 March 2020); https://doi.org/10.1117/12.2549889
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KEYWORDS
Cancer
Tissues
Tumors
Artificial intelligence
Genetics
Image analysis
Interdisciplinary fields
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