通过聚类发现乳腺癌风险模型中的重复影像特征。
Revealing Mammographic Phenotypes in Deep Learning Breast Cancer Risk Models
- 用预训练模型嵌入聚类，识别跨人群的重复影像模式。
- 高风险特征与致密组织、微钙化等结构相关，5年风险升高。
- 揭示了模型潜在混淆因子，适合临床与AI交叉研究者。
基于乳腺钼靶的深度学习模型提升了乳腺癌风险预测能力，但其学习到的影像模式仍缺乏深入探索。现有可解释性方法依赖单张图像的显著性图，无法识别大规模患者队列中反复出现的哺乳影像表型。通过聚类预训练模型Mirai的图像块嵌入，我们分离出与5年癌症风险相关的重复表型。分析显示，增加风险的表型捕捉了复杂结构（如致密组织、微钙化）和捷径伪影（如夹子）。这些表型与年龄较大及更高BI-RADS密度显著相关。该框架将组织模式与AI风险评分关联，揭示了临床特征及潜在的模型混淆因素。
原文摘要 · Abstract (English)
Mammogram-based deep learning models have improved breast cancer risk prediction, but the learned imaging patterns remain underexplored. Existing interpretability methods rely on single-image saliency maps, failing to identify recurring mammographic phenotypes across large patient cohorts. By clustering patch embeddings from a pre-trained model, Mirai, we isolate recurring phenotypes linked to 5-year cancer risk. Analyses show risk-increasing phenotypes capture complex structures (e.g., dense tissue, microcalcifications) and shortcut artifacts (e.g., clips). These phenotypes correlate strongly with older age and higher BI-RADS density. Our framework connects tissue patterns to AI risk scores, revealing clinical signatures and potential latent model confounders.
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