  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.
