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Advancing Histopathology-Based Breast Cancer Diagnosis: Insights into Multi-Modality and Explainability

Faseela Abdullakutty, Younes Akbari, Somaya Al-Maadeed, Ahmed Bouridane, Rifat Hamoudi

TL;DR

Breast cancer diagnosis benefits from integrating histopathology with non-image data, addressing the limitations of unimodal approaches. This review surveys multi-modal fusion techniques and explainability frameworks for histopathology-based diagnosis, detailing datasets, fusion architectures, and XAI methods. It identifies gaps in data integration, fusion strategies, and interpretability, and advocates a unified, explainable pipeline to translate multimodal insights into clinical practice. The work aims to enhance diagnostic accuracy, clinician trust, and enable personalized treatment planning.

Abstract

It is imperative that breast cancer is detected precisely and timely to improve patient outcomes. Diagnostic methodologies have traditionally relied on unimodal approaches; however, medical data analytics is integrating diverse data sources beyond conventional imaging. Using multi-modal techniques, integrating both image and non-image data, marks a transformative advancement in breast cancer diagnosis. The purpose of this review is to explore the burgeoning field of multimodal techniques, particularly the fusion of histopathology images with non-image data. Further, Explainable AI (XAI) will be used to elucidate the decision-making processes of complex algorithms, emphasizing the necessity of explainability in diagnostic processes. This review utilizes multi-modal data and emphasizes explainability to enhance diagnostic accuracy, clinician confidence, and patient engagement, ultimately fostering more personalized treatment strategies for breast cancer, while also identifying research gaps in multi-modality and explainability, guiding future studies, and contributing to the strategic direction of the field.

Advancing Histopathology-Based Breast Cancer Diagnosis: Insights into Multi-Modality and Explainability

TL;DR

Breast cancer diagnosis benefits from integrating histopathology with non-image data, addressing the limitations of unimodal approaches. This review surveys multi-modal fusion techniques and explainability frameworks for histopathology-based diagnosis, detailing datasets, fusion architectures, and XAI methods. It identifies gaps in data integration, fusion strategies, and interpretability, and advocates a unified, explainable pipeline to translate multimodal insights into clinical practice. The work aims to enhance diagnostic accuracy, clinician trust, and enable personalized treatment planning.

Abstract

It is imperative that breast cancer is detected precisely and timely to improve patient outcomes. Diagnostic methodologies have traditionally relied on unimodal approaches; however, medical data analytics is integrating diverse data sources beyond conventional imaging. Using multi-modal techniques, integrating both image and non-image data, marks a transformative advancement in breast cancer diagnosis. The purpose of this review is to explore the burgeoning field of multimodal techniques, particularly the fusion of histopathology images with non-image data. Further, Explainable AI (XAI) will be used to elucidate the decision-making processes of complex algorithms, emphasizing the necessity of explainability in diagnostic processes. This review utilizes multi-modal data and emphasizes explainability to enhance diagnostic accuracy, clinician confidence, and patient engagement, ultimately fostering more personalized treatment strategies for breast cancer, while also identifying research gaps in multi-modality and explainability, guiding future studies, and contributing to the strategic direction of the field.
Paper Structure (11 sections, 6 figures, 4 tables)

This paper contains 11 sections, 6 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Breast Cancer Diagnosis: An Overview
  3. Tasks
  4. Datasets
  5. Exploring multi-modality
  6. Multi-Modal Breast Cancer Diagnosis: Combining Histopathology and Non-Image Modalities
  7. Explainable breast cancer detection: Challenges, trends and future directions
  8. An explainability analysis on multi-modal techniques
  9. New frontiers in explainability and multi-modality
  10. Unified Framework: Leveraging Multi-Modal and Explainability
  11. Conclusion

Figures (6)

  • Figure 1: A generic representation of breast cancer diagnosis
  • Figure 2: Types of breast cancer diagnosis data
  • Figure 3: Breast Cancer diagnosis tasks
  • Figure 4: Multi-modal approaches
  • Figure 5: Different types of XAI methods
  • ...and 1 more figures