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Proceedings Paper

Multimodal fusion of histology and molecular features for improved survival outcome prediction (Conference Presentation)

Paper Abstract

Despite holding enormous potential in elucidating the tumor microenvironment and its phenotypic morphological heterogeneity, whole-slide image slides are underutilized in the analysis of survival outcomes and biomarker discovery, with very few methods developed that seek to integrate transcriptome profiles with histopathology data. In this work, we propose to fuse of molecular and histology features using artificial intelligence, and train an end-to-end multimodal deep neural network for survival outcome prediction. Our research establishes insight and theory on how to combine multimodal biomedical data, which will be integral for other problems in medicine with heterogenous data sources.

Paper Details

Date Published: 17 March 2020
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Proc. SPIE 11320, Medical Imaging 2020: Digital Pathology, 113200N (17 March 2020); doi: 10.1117/12.2549855
Show Author Affiliations
Richard J. Chen, Harvard Medical School (United States)
Brigham and Women's Hospital (United States)
Max Lu, 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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