Improving Domain Transfer with Consistency-Regularized Joint Distribution Alignment for Medical Image Classification

Background: Domain transfer plays a vital role in medical image analysis. It mitigates the challenges posed by variations in imaging equipment, protocols, and patient demographics, ultimately improving model performance across different domains or edge-intelligence devices; Methods: This paper introduces a new unsupervised domain adaptation approach, named Consistency-regularized Joint Distribution Alignment (C-JDA). Specifically, our method leverages Convolutional Neural Networks (CNNs) to align the joint distributions of source and target domains in the feature space, which involves the pseudo-labels of the target data for computing the relative chi-square divergence to measure the distribution relationship or asymmetry. Compared with traditional alignment methods with complex architectures or adversarial training, our model can be solved with a close-form equation, which is convenient for transferring among various scenarios. Additionally, we further propose symmetric consistency regularization to improve the robustness of the pseudo-label generation with diverse data augmentation strategies, where the augmented data are symmetric to their original data and should share the same predictions. Therefore, both components between distribution alignment and pseudo-label generation can be mutually improved for better performance. Results: Extensive experiments on multiple public medical image benchmarks demonstrate that C-JDA consistently outperforms both traditional domain adaptation methods and deep learning-based approaches. For the colon disease classification task, C-JDA achieved an accuracy of 87.41%, outperforming existing methods by 3.31%, with an F1 score of 87.26% and an improvement of 2.99%. For the Diabetic Retinopathy (DR) classification task, our method attained an accuracy and F1 score of 96.93%, surpassing state-of-the-art methods by 2.4%. Additionally, ablation studies validated the effectiveness of both the joint distribution alignment and symmetric consistency regularization components. Conclusions: Our C-JDA can significantly outperform existing domain adaptation methods by achieving state-of-the-art performance via improved joint distribution alignment with symmetric consistency regularization.