Learning from heterogeneous structural MRI via collaborative domain adaptation for late-Life depression assessment

Accurate identification of late-life depression (LLD) using structural brain MRI is essential for monitoring disease progression and facilitating timely intervention. However, existing learning-based approaches for LLD detection are often constrained by limited sample sizes (e.g., tens), which pose significant challenges for reliable model training and generalization. Although incorporating auxiliary datasets can expand the training set, substantial domain heterogeneity, such as differences in imaging protocols, scanner hardware, and population demographics, often undermines cross-domain transferability. To address this issue, we propose a Collaborative Domain Adaptation (CDA) framework for LLD detection using T1-weighted MRIs. The CDA leverages a dual-branch architecture integrating a Vision Transformer (ViT) and a Convolutional Neural Network (CNN) to exploit their complementary strengths in capturing global anatomical topology and local texture details. The CDA framework consists of three stages: (a) supervised training on labeled source data, (b) self-supervised target feature adaptation to refine decision boundaries, and (c) collaborative training on unlabeled target data. The collaborative stage employs a reliability-aware JSD-based dual-constraint mechanism to filter high-quality pseudo-labels, encouraging robust prediction consistency without target supervision. Extensive experiments on two multi-site benchmarks demonstrate that CDA consistently outperforms state-of-the-art unsupervised domain adaptation methods, highlighting its superior generalization ability for real applications.