融合乳腺影像与临床数据，提升癌症诊断准确率。
Cross-Attention Multimodal Fusion for Breast Cancer Diagnosis: Integrating Mammography and Clinical Data with Explainability
- 采用交叉注意力机制融合影像与临床数据
- 在公开数据集上达到0.98的AUC-ROC
- 模型结果可解释，适合临床辅助决策
精准评估乳腺病灶风险可显著降低发病率，并帮助医生制定最优治疗方案。目前大多数计算机辅助系统仅依赖乳腺钼靶图像特征进行分类，虽具实用性，却未能充分挖掘临床报告中的宝贵信息以实现最佳效果。相比仅使用钼靶图像，临床特征是否能显著提升病灶分类性能？如何有效融合临床特征与钼靶图像？可解释AI方法能否增强乳腺癌诊断模型的可解释性与可靠性？为回答这些问题，本研究系统考察了基于特征拼接、协同注意力和交叉注意力的多种多模态深度网络。在公开数据集TCGA和CBIS-DDSM上，模型表现优异，达到AUC-ROC 0.98，准确率0.96，F1分数0.94，精确率0.92，召回率0.95。
原文摘要 · Abstract (English)
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).
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