混合跨模态注意力网络用于低资源临床环境下的早期乳腺癌检测
Hybrid Cross-Modal Attention Network for Early Breast Cancer Detection in Low-Resource Clinical Settings
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中文总结 AI 辅助
针对非洲低资源环境，提出混合跨模态注意力网络HCMAN，整合乳腺图像与临床数据，在本地数据集上达97.8%准确率，且对低质量图像鲁棒，实现轻量级部署。
中文摘要 AI 辅助
乳腺癌是撒哈拉以南非洲地区女性癌症相关死亡的主要原因，该地区因放射学专业知识有限和临床数据系统碎片化而导致诊断延迟。尽管深度学习模型在乳腺X线摄影分析中展现出强大性能，但大多数模型仅依赖影像数据，并在西方人群上训练，限制了其在非洲医疗环境中的适用性。本文提出一种混合跨模态注意力网络（HCMAN），利用基于Transformer的跨模态注意力机制将乳腺X线摄影图像与结构化临床数据相整合。该模型使用从四家埃塞俄比亚转诊医院收集的1,024名患者的2,560张乳腺X线摄影图像的本地数据集进行开发和验证，并带有活检确认的金标准标签。所提出的框架实现了97.8%的准确率、97.2%的灵敏度、98.3%的特异性和0.987的AUC，显著优于仅基于图像的基线模型。该系统对资源有限环境中典型的低质量图像表现出鲁棒性，性能下降仅为3.2%，而仅基于图像的模型为8.7%。跨模态注意力分析揭示了临床合理的行为：对于致密乳腺和年轻患者等模糊病例，更依赖于临床特征。该模型的轻量级架构使其能够在标准医院工作站上部署（CPU上推理时间<2秒）。这项工作推动了可持续、情境感知的AI解决方案，以促进非洲公平的乳腺癌诊断。
英文摘要
Breast cancer is the leading cause of cancer-related mortality among women in Sub-Saharan Africa, where delayed diagnosis results from limited radiology expertise and fragmented clinical data systems. Although deep learning models have demonstrated strong performance in mammographic analysis, most rely solely on imaging data and are trained on Western populations, limiting their applicability in African healthcare settings. This paper presents a Hybrid Cross-Modal Attention Network (HCMAN) that integrates mammogram images with structured clinical data using transformer-based cross-modal attention mechanisms. The model was developed and validated using a locally collected dataset of 2,560 mammogram images from 1,024 patients across four Ethiopian referral hospitals, with biopsy-confirmed ground truth labels. The proposed framework achieves 97.8% accuracy, 97.2% sensitivity, 98.3% specificity, and an AUC of 0.987, significantly outperforming image-only baselines. The system demonstrates robustness to low-quality images typical of resource-limited settings, with only 3.2% performance degradation compared to 8.7% for image-only models. Cross-modal attention analysis reveals clinically appropriate behavior: higher reliance on clinical features for ambiguous cases such as dense breasts and young patients. The model's lightweight architecture enables deployment on standard hospital workstations (<2 seconds inference on CPU). This work advances sustainable, context-aware AI solutions for equitable breast cancer diagnostics in Africa.
发表机构
- Mekelle University（默克莱大学）
机构由 AI 辅助整理，请以论文原文为准。
