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Related Experiment Video

Updated: May 4, 2026

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
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A multimodal data-based model for breast cancer diagnosis.

Huina Wang1, Lan Wei2, Jianqiang Li3

  • 1School of Computer Science, Beijing University of Technology, Beijing, 100124, China; FutureNeuro Research Ireland Centre, School of Computer Science, University College Dublin, Dublin, D04 V1W8, Ireland.

Computer Methods and Programs in Biomedicine
|February 27, 2026
PubMed
Summary

This study introduces the Feature Enhancement and Semantic Collaborative Alignment (FESCA) framework for improved breast cancer diagnosis using multimodal data. FESCA enhances feature representation and cross-modal alignment, outperforming existing methods for accurate classification.

Keywords:
Contrastive learningCross-modal learningDiagnostic systemsMultimodal classification

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Area of Science:

  • Computational biology
  • Medical informatics
  • Oncology

Background:

  • Multimodal data-driven diagnostic systems are crucial for improving breast cancer outcomes.
  • Challenges in multimodal data analysis include semantic heterogeneity, scale discrepancies, and cross-modal alignment difficulties.
  • Existing fusion methods often fail to capture fine-grained semantics and complex interactions between histopathological and genomic data.

Purpose of the Study:

  • To develop an advanced multimodal diagnostic framework for breast cancer.
  • To address limitations in current multimodal feature fusion and cross-modal alignment techniques.
  • To improve the accuracy and robustness of breast cancer classification using integrated histopathological and genomic data.

Main Methods:

  • Propose the Feature Enhancement and Semantic Collaborative Alignment (FESCA) framework.
  • Utilize a semantic-guided modality feature enhancement mechanism for pathological images and genomic data.
  • Employ contrastive learning for cross-modal alignment into a unified semantic space.
  • Implement a multimodal collaborative diagnostic strategy for adaptive feature representation.

Main Results:

  • FESCA demonstrates superior performance in breast cancer classification compared to state-of-the-art methods on the TCGA-BRCA dataset.
  • The framework significantly enhances intra-modality representation quality.
  • Improved cross-modal semantic alignment is achieved through the proposed methods.

Conclusions:

  • The FESCA framework offers a robust approach for multimodal breast cancer diagnosis.
  • A web-based system visualizing the FESCA model was developed for practical clinical application.
  • This work provides a benchmark for future multimodal diagnostic system development in oncology.