标题： 跨注意力多模态融合用于乳腺癌诊断：结合乳腺X线摄影和临床数据的可解释性方法 显示英文标题

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

摘要： 对乳腺病变风险的精确评估可以大大降低风险，并帮助医生选择最佳的治疗方案。 为了对乳腺病变进行分类，目前大多数计算机辅助系统仅使用来自乳腺X线照片的特征。 尽管这种方法是实用的，但它并未充分利用临床报告中的宝贵信息以获得最佳结果。 与仅使用乳腺X线照片相比，临床特征是否能显著提高乳腺病变的分类？ 临床特征和乳腺X线照片如何最有效地结合？ 可解释的人工智能方法在哪些方面可以提高用于诊断乳腺癌的模型的可解释性和可靠性？ 为了解答这些基本问题，迫切需要进行全面的研究。 为了整合乳腺X线照片和分类的临床特征，本研究考察了基于特征拼接、共同注意和交叉注意的多种多模态深度网络。 该模型在公开数据集（TCGA和CBIS-DDSM）上测试时，AUC-ROC达到0.98，准确率为0.96， F1分数为0.94，精确率为0.92，召回率为0.95。 显示英文摘要

摘要： 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).