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Glaucoma is one of the ophthalmic diseases that may cause blindness, forwhich early detection and treatment are very important. Fundus images andoptical coherence tomography (OCT) images are both widely-used modalities indiagnosing glaucoma. However, existing glaucoma grading approaches mainlyutilize a single modality, ignoring the complementary information betweenfundus and OCT. In this paper, we propose an efficient multi-modalitysupervised contrastive learning framework, named COROLLA, for glaucoma grading.Through layer segmentation as well as thickness calculation and projection,retinal thickness maps are extracted from the original OCT volumes and used asa replacing modality, resulting in more efficient calculations with less memoryusage. Given the high structure and distribution similarities across medicalimage samples, we employ supervised contrastive learning to increase ourmodels' discriminative power with better convergence. Moreover, feature-levelfusion of paired fundus image and thickness map is conducted for enhanceddiagnosis accuracy. On the GAMMA dataset, our COROLLA framework achievesoverwhelming glaucoma grading performance compared to state-of-the-art methods.

COROLLA: An Efficient Multi-Modality Fusion Framework with Supervised Contrastive Learning for Glaucoma Grading