Contrastive learning, a prominent approach within self-supervised learning, has demonstrated significant effectiveness in developing generalizable models for various applications involving natural images. However, recent research indicates that these successes do not necessarily extend to the medical imaging domain. In this paper, we investigate the reasons for this suboptimal performance and hypothesize that the dense distribution of medical images poses challenges to the pretext tasks in contrastive learning, particularly in constructing positive and negative pairs. We explore model performance under different augmentation strategies and compare the results to those achieved with strong augmentations. Our study includes six publicly available datasets covering multiple clinically relevant tasks. We further assess the model's generalizability through external evaluations. The model pre-trained with weak augmentation outperforms those with strong augmentation, improving AUROC from 0.838 to 0.848 and AUPR from 0.523 to 0.597 on MESSIDOR2, and showing similar enhancements across other datasets. Our findings suggest that optimizing the scale of augmentation is critical for enhancing the efficacy of contrastive learning in medical imaging.

本研究解决了对比学习在医学成像领域效果不佳的问题，探讨了医疗图像密集分布对构建正负样本对的挑战。研究提出了一种新的增强策略，发现使用弱增强进行预训练的模型在多个数据集上表现更佳，尤其是在AUROC和AUPR值上有显著提升，强调了优化增强尺度对于提高对比学习在医学成像中有效性的关键性。

通过调整增强尺度提升视网膜成像对比学习的效果