  Automatic ophthalmic disease diagnosis on fundus images is important in
clinical practice. However, due to complex fundus textures and limited
annotated data, developing an effective automatic method for this problem is
still challenging. In this paper, we present a self-supervised method via polar
transformation based progressive contrastive learning, called PoCo, for
ophthalmic disease diagnosis. Specifically, we novelly inject the polar
transformation into contrastive learning to 1) promote contrastive learning
pre-training to be faster and more stable and 2) naturally capture task-free
and rotation-related textures, which provides insights into disease recognition
on fundus images. Beneficially, simple normal translation-invariant convolution
on transformed images can equivalently replace the complex rotation-invariant
and sector convolution on raw images. After that, we develop a progressive
contrastive learning method to efficiently utilize large unannotated images and
a novel progressive hard negative sampling scheme to gradually reduce the
negative sample number for efficient training and performance enhancement.
Extensive experiments on three public ophthalmic disease datasets show that our
PoCo achieves state-of-the-art performance with good generalization ability,
validating that our method can reduce annotation efforts and provide reliable
diagnosis. Codes are available at \url{https://github.com/wjh892521292/PoCo}.
