  Lesion appearance is a crucial clue for medical providers to distinguish
referable diabetic retinopathy (rDR) from non-referable DR. Most existing
large-scale DR datasets contain only image-level labels rather than pixel-based
annotations. This motivates us to develop algorithms to classify rDR and
segment lesions via image-level labels. This paper leverages self-supervised
equivariant learning and attention-based multi-instance learning (MIL) to
tackle this problem. MIL is an effective strategy to differentiate positive and
negative instances, helping us discard background regions (negative instances)
while localizing lesion regions (positive ones). However, MIL only provides
coarse lesion localization and cannot distinguish lesions located across
adjacent patches. Conversely, a self-supervised equivariant attention mechanism
(SEAM) generates a segmentation-level class activation map (CAM) that can guide
patch extraction of lesions more accurately. Our work aims at integrating both
methods to improve rDR classification accuracy. We conduct extensive validation
experiments on the Eyepacs dataset, achieving an area under the receiver
operating characteristic curve (AU ROC) of 0.958, outperforming current
state-of-the-art algorithms.
