计算机科学
人工智能
保险丝(电气)
结果(博弈论)
机器学习
人工神经网络
模式识别(心理学)
工程类
数学
电气工程
数理经济学
作者
Richard J. Chen,Max Lu,Faisal Mahmood
摘要
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.
科研通智能强力驱动
Strongly Powered by AbleSci AI