Domain Generalization for Medical Imaging Classification with Linear-Dependency Regularization

TL;DR

This paper proposes a linear-dependency regularization method to enhance the domain generalization of deep neural networks in medical imaging classification, enabling better performance on unseen data from different domains.

Contribution

Introduces a novel linear-dependency regularization to learn a shared feature space, improving cross-domain generalization in medical imaging classification.

Findings

Outperforms state-of-the-art baselines in cross-domain tasks

Improves generalization on unseen medical data

Effective with limited training datasets

Abstract

Recently, we have witnessed great progress in the field of medical imaging classification by adopting deep neural networks. However, the recent advanced models still require accessing sufficiently large and representative datasets for training, which is often unfeasible in clinically realistic environments. When trained on limited datasets, the deep neural network is lack of generalization capability, as the trained deep neural network on data within a certain distribution (e.g. the data captured by a certain device vendor or patient population) may not be able to generalize to the data with another distribution. In this paper, we introduce a simple but effective approach to improve the generalization capability of deep neural networks in the field of medical imaging classification. Motivated by the observation that the domain variability of the medical images is to some extent compact, we propose to learn a representative feature space through variational encoding with a novel linear-dependency regularization term to capture the shareable information among medical data collected from different domains. As a result, the trained neural network is expected to equip with better generalization capability to the "unseen" medical data. Experimental results on two challenging medical imaging classification tasks indicate that our method can achieve better cross-domain generalization capability compared with state-of-the-art baselines.