发表机构
Beijing University of Posts and Telecommunications（北京邮电大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对医学成像模型缺失元数据问题，提出CAPRA框架，通过预测语义轴、校准轴后验等操作，构建校准子组接口，能揭示差异模式，产生更优子组划分，还可被下游学习者重用，实现缺失元数据下隐藏子组分析的校准、可解释与可重用。
AI 中文摘要
医学成像模型在部署时往往缺少用于子组审核的人口统计学、采集和质量元数据。当这些元数据缺失时，临床关键的失败模式可能会被良好的总体性能掩盖，许多稳健学习方法也会失去其所依赖的组结构。我们提出了CAPRA，一个用于缺失元数据下隐藏子组分析的校准代理轴框架。CAPRA预测图像衍生的语义轴，通过患者级交叉拟合在小的元数据标记分割上校准轴后验，并将这些后验组织成一个校准的子组接口，支持部署时的失败分析和下游稳健学习，而无需在部署时提供子组标签。在眼底、皮肤镜和胸部X光成像中，CAPRA揭示了仅靠元数据切片遗漏的差异模式，在数据集变化时仍保持信息性，并产生比仅图像或潜在切片基线更紧密对齐明确失败轴的子组划分。同一接口也可被下游稳健学习者重用，不过这些收益因领域而异。总体而言，CAPRA将缺失元数据下的隐藏子组分析转变为一个用于部署时分析和稳健转移的校准、可解释且可重用的子组接口。
英文摘要
Medical imaging models are often deployed without the demographic, acquisition, and quality metadata needed for subgroup auditing. Once those metadata disappear, clinically critical failure modes can be masked by strong aggregate performance, and many robust-learning methods lose the group structure they rely on. We present CAPRA, a calibrated proxy-axis framework for hidden subgroup analysis under missing metadata. CAPRA predicts image-derived semantic axes, calibrates axis posteriors on a small metadata-labeled split via patient-level cross-fitting, and organizes those posteriors into a calibrated subgroup interface that supports both deployment-time failure analysis and downstream robust learning without requiring subgroup labels at deployment. Across fundus, dermoscopy, and chest radiography, CAPRA reveals disparity patterns missed by metadata-only slicing, remains informative under dataset shift, and produces subgroup partitions that align more closely with explicit failure axes than image-only or latent-slice baselines. The same interface can also be reused by downstream robust learners, although those gains are domain-dependent. Overall, CAPRA turns hidden subgroup analysis under missing metadata into a calibrated, interpretable, and reusable subgroup interface for deployment-time analysis and robust transfer.
