  Glaucoma is a chronic neurodegenerative condition that can lead to blindness.
Early detection and curing are very important in stopping the disease from
getting worse for glaucoma patients. The 2D fundus images and optical coherence
tomography(OCT) are useful for ophthalmologists in diagnosing glaucoma. There
are many methods based on the fundus images or 3D OCT volumes; however, the
mining for multi-modality, including both fundus images and data, is less
studied. In this work, we propose an end-to-end local and global multi-modal
fusion framework for glaucoma grading, named ELF for short. ELF can fully
utilize the complementary information between fundus and OCT. In addition,
unlike previous methods that concatenate the multi-modal features together,
which lack exploring the mutual information between different modalities, ELF
can take advantage of local-wise and global-wise mutual information. The
extensive experiment conducted on the multi-modal glaucoma grading GAMMA
dataset can prove the effiectness of ELF when compared with other
state-of-the-art methods.
