Deep neural networks (DNN) have demonstrated unprecedented success for medical imaging applications. However, due to the issue of limited dataset availability and the strict legal and ethical requirements for patient privacy protection, the broad applications of medical imaging classification driven by DNN with large-scale training data have been largely hindered. For example, when training the DNN from one domain (e.g., with data only from one hospital), the generalization capability to another domain (e.g., data from another hospital) could be largely lacking. In this paper, we aim to tackle this problem by developing the privacy-preserving constrained domain generalization method, aiming to improve the generalization capability under the privacy-preserving condition. In particular, We propose to improve the information aggregation process on the centralized server-side with a novel gradient alignment loss, expecting that the trained model can be better generalized to the "unseen" but related medical images. The rationale and effectiveness of our proposed method can be explained by connecting our proposed method with the Maximum Mean Discrepancy (MMD) which has been widely adopted as the distribution distance measurement. Experimental results on two challenging medical imaging classification tasks indicate that our method can achieve better cross-domain generalization capability compared to the state-of-the-art federated learning methods.

翻译：深度神经网络（DNN）已在医学影像应用中展现出前所未有的成功。然而，由于数据集可用性有限以及患者隐私保护相关的严格法律与伦理要求，大规模训练数据驱动的DNN在医学图像分类中的广泛应用受到了极大阻碍。例如，当仅从一个域（如仅使用某家医院的数据）训练DNN时，其向另一个域（如另一家医院的数据）的泛化能力可能严重不足。本文旨在通过开发隐私保护的约束域泛化方法解决这一问题，以提升隐私保护条件下的泛化能力。具体而言，我们提出在中央服务器端改进信息聚合过程，引入一种新颖的梯度对齐损失函数，期望训练后的模型能更好地泛化到“未见”但相关的医学图像。通过将所提方法与广泛用于分布距离度量的最大均值差异（MMD）建立联系，可解释其原理与有效性。在两个具有挑战性的医学图像分类任务上的实验结果表明，与最先进的联邦学习方法相比，我们的方法能实现更优的跨域泛化性能。