Towards Label-Efficient Deep Learning for Myopic Maculopathy Classification

Myopic Maculopathy is the leading cause of legal blindness in patients with pathologic myopia. Automated myopic maculopathy diagnosis is of vital importance to early treatment and progression slowdown. However, the scarcity of labeled fundus images with myopic maculopathy makes it challenging to improve diagnostic performance via deep learning models. In this paper, we construct a label-efficient deep learning framework for myopic maculopathy classification. In specific, we exploit two categories of pre-training methods, i.e., vision-language pre-training and self-supervised visual representation learning, to alleviate the overfitting problem caused by the limited number of training images. Moreover, we adopt a semi-supervised learning technique, namely pseudo labeling, to leverage a large number of unlabeled fundus images from external datasets. We also investigate the impact of other key components in model training for better performance, including backbone architecture, input resolution, and loss function. Our method is evaluated in the MICCAI 2023 Myopic Maculopathy Analysis Challenge (MMAC). Among 17 participating teams, our ensembled model ranked 1st on the leaderboard with an average score of 0.8752. The code will be publicly available at https://github.com/FDU-VTS/MMAC .