Cross-Modal Translation and Alignment for Survival Analysis

• URL: http://arxiv.org/abs/2309.12855v1
• Date: Fri, 22 Sep 2023 13:29:14 GMT
• Title: Cross-Modal Translation and Alignment for Survival Analysis
• Authors: Fengtao Zhou, Hao Chen
• Abstract summary: We present a framework to explore the intrinsic cross-modal correlations and transfer potential complementary information. Our experiments on five public TCGA datasets demonstrate that our proposed framework outperforms the state-of-the-art methods.
• Score: 7.657906359372181
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the rapid advances in high-throughput sequencing technologies, the focus of survival analysis has shifted from examining clinical indicators to incorporating genomic profiles with pathological images. However, existing methods either directly adopt a straightforward fusion of pathological features and genomic profiles for survival prediction, or take genomic profiles as guidance to integrate the features of pathological images. The former would overlook intrinsic cross-modal correlations. The latter would discard pathological information irrelevant to gene expression. To address these issues, we present a Cross-Modal Translation and Alignment (CMTA) framework to explore the intrinsic cross-modal correlations and transfer potential complementary information. Specifically, we construct two parallel encoder-decoder structures for multi-modal data to integrate intra-modal information and generate cross-modal representation. Taking the generated cross-modal representation to enhance and recalibrate intra-modal representation can significantly improve its discrimination for comprehensive survival analysis. To explore the intrinsic crossmodal correlations, we further design a cross-modal attention module as the information bridge between different modalities to perform cross-modal interactions and transfer complementary information. Our extensive experiments on five public TCGA datasets demonstrate that our proposed framework outperforms the state-of-the-art methods.

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