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

TL;DR

This paper reviews the integration of multi-modal data and explainable AI in breast cancer diagnosis, highlighting advancements, challenges, and future research directions to improve accuracy and clinician trust.

Contribution

It provides a comprehensive overview of multi-modal techniques and explainability in breast cancer diagnosis, identifying research gaps and guiding future innovations.

Findings

Multi-modal approaches enhance diagnostic accuracy.

Explainable AI increases clinician confidence.

Identifies key research gaps in the field.

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.