标题： 通过调整增强尺度提升视网膜成像的对比学习 显示英文标题

标题： Enhancing Contrastive Learning for Retinal Imaging via Adjusted Augmentation Scales

摘要： 对比学习是自监督学习中一种突出的方法，在开发适用于各种涉及自然图像的应用的可泛化模型方面表现出显著的有效性。 然而，最近的研究表明，这些成功并不一定适用于医学影像领域。 在本文中，我们研究了这种次优性能的原因，并假设医学图像的密集分布对对比学习中的预训练任务构成了挑战，特别是在构建正负对方面。 我们探索了不同增强策略下的模型性能，并将结果与使用强增强方法获得的结果进行比较。 我们的研究包括六个公开可用的数据集，涵盖了多个临床相关的任务。 我们进一步通过外部评估来评估模型的泛化能力。 使用弱增强预训练的模型优于使用强增强的模型，在MESSIDOR2数据集上，AUROC从0.838提高到0.848，AUPR从0.523提高到0.597，并在其他数据集中显示出类似的提升。 我们的研究结果表明，优化增强的规模对于提高对比学习在医学影像中的有效性至关重要。 显示英文摘要

摘要： 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.