  Glaucoma is one of the ophthalmic diseases that may cause blindness, for
which early detection and treatment are very important. Fundus images and
optical coherence tomography (OCT) images are both widely-used modalities in
diagnosing glaucoma. However, existing glaucoma grading approaches mainly
utilize a single modality, ignoring the complementary information between
fundus and OCT. In this paper, we propose an efficient multi-modality
supervised 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 as
a replacing modality, resulting in more efficient calculations with less memory
usage. Given the high structure and distribution similarities across medical
image samples, we employ supervised contrastive learning to increase our
models' discriminative power with better convergence. Moreover, feature-level
fusion of paired fundus image and thickness map is conducted for enhanced
diagnosis accuracy. On the GAMMA dataset, our COROLLA framework achieves
overwhelming glaucoma grading performance compared to state-of-the-art methods.
