Glaucoma is one of the leading causes of vision impairment. Digital imaging techniques, such as color fundus photography (CFP) and optical coherence tomography (OCT), provide quantitative and noninvasive methods for glaucoma diagnosis. Recently, in the field of computer-aided glaucoma diagnosis, multi-modality methods that integrate the CFP and OCT modalities have achieved greater diagnostic accuracy compared to single-modality methods. However, it remains challenging to extract reliable features due to the high similarity of medical images and the unbalanced multi-modal data distribution. Moreover, existing methods overlook the uncertainty estimation of different modalities, leading to unreliable predictions. To address these challenges, we propose a novel framework, namely ETSCL, which consists of a contrastive feature extraction stage and a decision-level fusion stage. Specifically, the supervised contrastive loss is employed to enhance the discriminative power in the feature extraction process, resulting in more effective features. In addition, we utilize the Frangi vesselness algorithm as a preprocessing step to incorporate vessel information to assist in the prediction. In the decision-level fusion stage, an evidence theory-based multi-modality classifier is employed to combine multi-source information with uncertainty estimation. Extensive experiments demonstrate that our method achieves state-of-the-art performance. The code is available at \url{https://github.com/master-Shix/ETSCL}.

Brain functional networks (BFNs) derived from multi-site fMRI data have been widely used in the diagnosis of psychiatric disorders such as schizophrenia (SZ). However, site-induced distribution shifts pose a major challenge to the generalization of classification models based on BFNs. While domain generalization (DG) seeks to address this challenge without access to target domain data, most methods make the idealized assumption that each class exhibits consistent structures across sites. The inherent diversity within each class often violates this assumption, resulting in poor generalization to unseen domains. To address this issue, we proposed a DG framework based on a transformer encoder combined with prototype learning. We first employed a transformer encoder to capture global dependencies among brain regions and extract discriminative subject-level representations from BFNs. A site-independence module with Hilbert-Schmidt Independence Criterion (HSIC) regularization was then applied to enforce site-invariant feature learning. Projected features were softly assigned to multiple prototypes via Sinkhorn matching, with prototypes updated via exponential moving average (EMA), and the maximum likelihood estimation (MLE) loss further refining feature-to-prototype matching probabilities. Contrastive and alignment losses were applied to promote inter-class separability and intra-class consistency, respectively. Experimental results showed that our method outperformed the other 7 DG, 6 domain adaptation (DA), 6 multi-site and 6 state-of-the-art methods across two independent SZ datasets (88.89%±2.22% and 86.05%±1.64%). Ablation results highlighted the contributions of the MLE, contrastive, and alignment constraints to the performance improvement. Furthermore, the identified discriminative temporal regions provided insights into the dysfunctional neural-mechanisms in SZ.