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17 March 2020 Multimodal fusion of histology and molecular features for improved survival outcome prediction (Conference Presentation)
Richard J. Chen, Max Lu, Faisal Mahmood
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Proceedings Volume 11320, Medical Imaging 2020: Digital Pathology; 113200N (2020) https://doi.org/10.1117/12.2549855
Event: SPIE Medical Imaging, 2020, Houston, Texas, United States
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.
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Richard J. Chen, Max Lu, and Faisal Mahmood "Multimodal fusion of histology and molecular features for improved survival outcome prediction (Conference Presentation)", Proc. SPIE 11320, Medical Imaging 2020: Digital Pathology, 113200N (17 March 2020); https://doi.org/10.1117/12.2549855
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KEYWORDS
Cancer
Image fusion
Tumors
Biological research
Biomedical optics
Feature extraction
Medical research
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