  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}.
