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 https://github.com/wjh892521292/PoCo.

通过基于极坐标转换的渐进对比学习的自监督方法，我们提出了一种用于眼科疾病诊断的自动方法。实验结果表明，我们的方法在三个公共眼科疾病数据集上达到了最先进的性能，具有良好的泛化能力，验证了我们的方法可以减少注释工作并提供可靠的诊断。