AI 中文总结
该研究针对超声心动图分割中Oracle条件设置误导部署的问题，提出互补间隙对方法，在CAMUS和EchoNet-Dynamic数据集上验证了条件可用性偏差，为部署时的模型选择与优化提供了依据。
AI 中文摘要
条件分割模型的训练与评估可能使用比部署时更干净的辅助信号，我们研究这种捷径学习和辅助变量偏移在相位条件超声心动图分割中的协议级表现。互补间隙对用于测量可部署Oracle估计路径的损失，并探测Oracle随机路径的敏感性。在保留的CAMUS数据集上，一次由Oracle选择的强循环运行在使用估计相位时严重失效，且三次运行均对错误相位保持敏感性。在EchoNet-Dynamic数据集上，当前估计器仍可用，但随机相位测试显示出强潜在敏感性。感知部署的检查点选择和相位扰动可缩小两种间隙，且平均Dice系数变化很小。探索性子组分析量化了各测量层的差异，下游射血分数（EF）审计显示，恢复分割不一定能恢复EF误差或符号偏差。总体而言，这些间隙用于测试Oracle条件下的性能是否能在部署时实际可用的推理路径中保持。
英文摘要
Conditional segmentation models may be trained and evaluated with auxiliary signals cleaner than those available at deployment. We study this protocol-level manifestation of shortcut learning and auxiliary-variable shift in phase-conditioned echocardiographic segmentation. The complementary gap pair measures loss on the deployable oracle-estimated pathway and probes sensitivity on the oracle-random pathway. On held-out CAMUS data, one strong-cyclic, oracle-selected run fails severely with estimated phase, while sensitivity to incorrect phase persists across three runs. On EchoNet-Dynamic, the current estimator remains usable, but random-phase testing reveals strong latent sensitivity. Deployment-aware checkpoint selection and phase perturbation reduce both gaps with little change in mean Dice. Exploratory subgroup analyses quantify variation across measured strata, and a downstream ejection fraction (EF) audit shows that recovering segmentation does not necessarily recover EF error or signed bias. Together, the gaps test whether oracle-conditioned performance survives the inference pathway actually available at deployment.
CommentsAccepted for publication in the MICCAI 2026 Workshop on Fairness of AI in Medical Imaging (FAIMI 2026). To appear in Springer Lecture Notes in Computer Science (LNCS)
