… By hand, eye disease diagnosis is labor-intensive, prone to mistakes, … ocular diseases such
as glaucoma (GA), diabetic retinopathy (DR), cataract (CT), and normal eye-related diseases …

… & Fattah, SAAn automatic ocular disease detection scheme from enhanced fundus images
based on ensembling deep cnn networks. In Proceedings of the 2020 11th International …

… A strategy to self-speculate the relevant information from CFPs is necessary for the … KD
inspired activation-based attention transfer between a deeper teacher network and a shallower …

… After briefly discussing the cause of the different ocular diseases, a gist of the major … × 7 for
Vision Transformer with self-attention, as shown in Fig. 3. Self-attention linearly expands each …

… , the goal of our ocular disease classification method is to … attention unit, we extend the
self-attention (1) to the region-guided attention module and construct the region-guided attention …

… Deep learning-based ocular image analysis can be used in … -label fundus images whereas
self-supervising learning is … In this manuscript, a novel attention-based model is proposed …

… -supervised feature learning techniques receive a lot of attention, as they do not … diseases
can greatly benefit from another imaging modality, eg, FFA, this paper presents a novel self-…

… Recent studies have verified that the ocular diseases, such as glaucoma, pathologic
myopia and maculopathy, and other related human diseases, such as high blood pressure, …

… Hence, in this paper, our goal is to present a self-supervised method, which learns the …
disease classification tasks. Recently, self-supervised learning has attracted increasing attention …

… interpretation of attention maps, especially if there are multiple levels of attention. Therefore,
… , which combines standard CNNs with a self-attention mechanism. However, when a …