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MIRROR: Multi-Modal Pathological Self-Supervised Representation Learning via Modality Alignment and Retention

Created by
  • Haebom

Author

Tianyi Wang, Jianan Fan, Dingxin Zhang, Dongnan Liu, Yong Xia, Heng Huang, Weidong Cai

Outline

This paper presents MIRROR, a novel method for multimodal self-supervised learning of histopathology and transcriptomics in cancer research. While existing multimodal integration methods focus on modal alignment, MIRROR simultaneously performs modal alignment while maintaining modal-specific structure by considering the heterogeneity of histopathology and transcriptomics. It builds a comprehensive cancer feature representation using a dedicated encoder that extracts features for each modality, a modal alignment module, a modal maintenance module, and a style clustering module. Experimental results using the TCGA cohort demonstrate excellent performance in cancer subtype classification and survival analysis.

Takeaways, Limitations

Takeaways:
A multimodal self-supervised learning method considering the heterogeneity of histopathology and transcriptomics is presented.
Achieving modal alignment and modal specific structure retention simultaneously
Demonstrated excellent performance in cancer subtype classification and survival analysis
Presenting the possibility of building a comprehensive cancer signature representation
Limitations:
Further validation of the generalization performance of the proposed method is needed.
Performance evaluation on other cancer types or other datasets is needed.
Lack of detailed description of inter-module interactions and optimization strategies.
Lack of quantitative measurement and analysis of modal-specific structural maintenance and modal alignment.
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