Cross-modal alignment and contrastive learning for enhanced cancer survival prediction.

Journal: Computer methods and programs in biomedicine
PMID: 39961170

Abstract

BACKGROUND AND OBJECTIVE: Integrating multimodal data, such as pathology images and genomics, is crucial for understanding cancer heterogeneity, personalized treatment complexity, and enhancing survival prediction. However, most current prognostic methods are limited to a single domain of histopathology or genomics, inevitably reducing their potential for accurate patient outcome prediction. Despite advancements in the concurrent analysis of pathology and genomic data, existing approaches inadequately address the intricate intermodal relationships.

Authors

  • Tengfei Li
    University of North Carolina, Chapel Hill, NC, USA.
  • Xuezhong Zhou
    School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China.
  • Jingyan Xue
    School of Computer Science and Technology, Beijing Jiaotong University, Beijing, 100044, China.
  • Lili Zeng
    School of Computer Science and Technology, Beijing Jiaotong University, Beijing, 100044, China.
  • Qiang Zhu
  • Ruiping Wang
  • Haibin Yu
    School of Electronics and Information, Hangzhou Dianzi University, Hangzhou 310018, China.
  • Jianan Xia
    Institute of Medical Intelligence, School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China.

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