融合影像与临床数据，提升乳腺癌诊断准确率
OncoVision: Integrating Mammography and Clinical Data through Attention-Driven Multimodal AI for Enhanced Breast Cancer Diagnosis
- 用注意力机制联合分析乳腺影像和临床信息
- 精准分割四类病灶并预测十项临床指标
- 适合放射科医生使用，助力基层医疗普及
OncoVision 是一个整合乳腺钼靶图像与临床数据的多模态AI流程。采用基于注意力机制的编码器-解码器架构，可同时对四类区域（肿块、钙化、腋窝发现、乳腺组织）进行高精度分割，并预测十项结构化临床特征：肿块形态、钙化类型、ACR乳腺密度及BI-RADS分类。通过两种后融合策略融合影像与临床信息，显著提升诊断精确度，降低观察者间差异。系统部署为安全、易用的Web应用，生成带双重置信度评分和注意力加权可视化结果的结构化报告，支持实时诊断辅助，增强医生信任感，适用于医学教学。该方案可便捷嵌入临床，使偏远地区如南亚农村也能实现筛查，推动早期发现与及时干预，为乳腺癌筛查提供可扩展、公平的AI解决方案。
原文摘要 · Abstract (English)
OncoVision is a multimodal AI pipeline that combines mammography images and clinical data for better breast cancer diagnosis. Employing an attention-based encoder-decoder backbone, it jointly segments four ROIs - masses, calcifications, axillary findings, and breast tissues - with state-of-the-art accuracy and robustly predicts ten structured clinical features: mass morphology, calcification type, ACR breast density, and BI-RADS categories. To fuse imaging and clinical insights, we developed two late-fusion strategies. By utilizing complementary multimodal data, late fusion strategies improve diagnostic precision and reduce inter-observer variability. Operationalized as a secure, user-friendly web application, OncoVision produces structured reports with dual-confidence scoring and attention-weighted visualizations for real-time diagnostic support to improve clinician trust and facilitate medical teaching. It can be easily incorporated into the clinic, making screening available in underprivileged areas around the world, such as rural South Asia. Combining accurate segmentation with clinical intuition, OncoVision raises the bar for AI-based mammography, offering a scalable and equitable solution to detect breast cancer at an earlier stage and enhancing treatment through timely interventions.
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