# Cross-Attention Multimodal Fusion for Breast Cancer Diagnosis: Integrating Mammography and Clinical Data with Explainability

arXiv: https://arxiv.org/abs/2508.16000 · HTML: https://mlsanity.com/paper/2508.16000/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 34 references that are in the map.
- First posted on arXiv 21 Aug 2025.

## Builds on (most cited first, 11 of 11)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks](https://mlsanity.com/paper/1908.02265/) (2019)
- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [Towards A Rigorous Science of Interpretable Machine Learning](https://mlsanity.com/paper/1702.08608/) (2017)
- [End-to-end Training for Whole Image Breast Cancer Diagnosis using An All Convolutional Design](https://mlsanity.com/paper/1711.05775/) (2017)
- [Detecting and classifying lesions in mammograms with Deep Learning](https://mlsanity.com/paper/1707.08401/) (2017)
- [Multimodal Categorization of Crisis Events in Social Media](https://mlsanity.com/paper/2004.04917/) (2020)
- [Adversarially Robust Feature Learning for Breast Cancer Diagnosis](https://mlsanity.com/paper/2402.08768/) (2024)
- [Enhancing Breast Cancer Diagnosis in Mammography: Evaluation and Integration of Convolutional Neural Networks and Explainable AI](https://mlsanity.com/paper/2404.03892/) (2024)
- [Multimodal Breast Lesion Classification Using Cross-Attention Deep Networks](https://mlsanity.com/paper/2108.09591/) (2021)
- [Comparative Analysis and Ensemble Enhancement of Leading CNN Architectures for Breast Cancer Classification](https://mlsanity.com/paper/2410.03333/) (2024)

## Abstract (arXiv)

A precise assessment of the risk of breast lesions can greatly lower it and assist physicians in choosing the best course of action. To categorise breast lesions, the majority of current computer-aided systems only use characteristics from mammograms. Although this method is practical, it does not completely utilise clinical reports' valuable information to attain the best results. When compared to utilising mammography alone, will clinical features greatly enhance the categorisation of breast lesions? How may clinical features and mammograms be combined most effectively? In what ways may explainable AI approaches improve the interpretability and reliability of models used to diagnose breast cancer? To answer these basic problems, a comprehensive investigation is desperately needed. In order to integrate mammography and categorical clinical characteristics, this study examines a number of multimodal deep networks grounded on feature concatenation, co-attention, and cross-attention. The model achieved an AUC-ROC of 0.98, accuracy of 0.96, F1-score of 0.94, precision of 0.92, and recall of 0.95 when tested on publicly accessible datasets (TCGA and CBIS-DDSM).