Privacy-Preserving Constrained Domain Generalization via Gradient Alignment
This addresses the challenge of generalizing deep neural networks across different medical domains while preserving patient privacy, though it appears incremental as it builds on existing federated learning and domain generalization techniques.
The paper tackles the problem of limited dataset availability and patient privacy in medical imaging by developing a privacy-preserving constrained domain generalization method, which improves cross-domain generalization capability compared to state-of-the-art federated learning methods on two medical imaging classification tasks.
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.