Abstract
Retinopathy of prematurity (ROP) is currently one of the leading causes of infant blindness worldwide. Recently significant progress has been made in deep learning-based computer-aided diagnostic methods. However, deep learning often requires a large amount of annotated data for model optimization, but this requires long hours of effort by experienced doctors in clinical scenarios. In contrast, a large number of unlabeled images are relatively easy to obtain. In this paper, we propose a new semi-supervised learning framework to reduce annotation costs for automatic ROP staging. We design two consistency regularization strategies, prediction consistency loss and semantic structure consistency loss, which can help the model mine useful discriminative information from unlabeled data, thus improving the generalization performance of the classification model. Extensive experiments on a real clinical dataset show that the proposed method promises to greatly reduce the labeling requirements in clinical scenarios while achieving good classification performance.