Learning the ability to generalize knowledge between similar contexts is particularly important in medical imaging as data distributions can shift substantially from one hospital to another, or even from one machine to another. To strengthen generalization, most state-of-the-art techniques inject knowledge of the data distribution shifts by enforcing constraints on learned features or regularizing parameters. We offer an alternative approach: Learning from Privileged Medical Imaging Information (LPMII). We show that using some privileged information such as tumor shape or location leads to stronger domain generalization ability than current state-of-the-art techniques. This paper demonstrates that by using privileged information to predict the severity of intra-layer retinal fluid in optical coherence tomography scans, the classification accuracy of a deep learning model operating on out-of-distribution data improves from $0.911$ to $0.934$. This paper provides a strong starting point for using privileged information in other medical problems requiring generalization.

翻译：学习在相似场景间泛化知识的能力在医学影像领域尤为重要，因为数据分布可能因医院差异甚至设备差异而发生显著变化。为增强泛化能力，大多数先进技术通过约束学习特征或正则化参数来注入数据分布偏移的知识。我们提出一种替代方法：通过特权医学影像信息学习（LPMII）。研究表明，利用肿瘤形状或位置等特权信息，可获得比当前先进技术更强的域泛化能力。本文证明，通过利用特权信息预测光学相干断层扫描中视网膜内层液体严重程度，深度学习模型在分布外数据上的分类准确率从0.911提升至0.934。本文为在其他需要泛化能力的医学问题中应用特权信息提供了坚实起点。