面向视网膜眼底图像分类的领域特定自监督表示学习
Domain-Specific Self-Supervised Representation Learning for Retinal Fundus Classification
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中文总结 AI 辅助
该研究针对医学标注数据稀缺问题，探究SimSiam与SimCLR在视网膜眼底图像分类中的应用，发现定制数据增强策略可提升性能，轻量级SSL框架能减少对大型标注数据的依赖并取得竞争力结果。
中文摘要 AI 辅助
尽管公开数据集数量不断增长，但带标注的医学图像仍然稀缺。监督学习方法在许多基准测试中表现出色，然而需要大量标注数据，而在医学领域获取这些数据成本高昂且耗时。为解决这一局限，对比自监督学习（SSL）已成为从无标注数据中学习有用表示的有前景替代方案。本研究针对基于眼底图像的视网膜疾病分类，探究了两种SSL框架：SimSiam与SimCLR。我们重点研究在资源受限场景下，数据增强策略和训练参数如何影响表示学习。鉴于数据和计算能力有限，我们探索了结合视网膜专用增强技术、使用小批量大小训练SSL模型的可行性。通过一系列实验，我们在下游任务（包括多疾病分类和糖尿病视网膜病变分级）中，采用线性评估和微调来评估所学表示的质量。结果表明，针对视网膜图像特性定制数据增强策略对提升性能至关重要；即便在受限场景下，轻量级SSL框架也能学习到可迁移的表示，减少对大型标注数据集的依赖并取得有竞争力的结果。
英文摘要
Despite the growing number of public datasets, annotated medical images remain scarce. Supervised learning methods achieve strong performance on many benchmarks, however require large amounts of labeled data, which are costly and time-consuming to obtain in the medical domain. To address this limitation, contrastive self-supervised learning (SSL) has emerged as a promising alternative for learning useful representations from unlabeled data. In this work, we investigate two SSL frameworks, SimSiam and SimCLR, for retinal disease classification from fundus images. We focus on understanding how augmentation strategies and training parameters influence representation learning under resource-constrained settings. Given limited data and computational capacity, we explore the feasibility of training SSL models with small batch sizes incorporated with retinal-specific augmentation techniques. Through a series of experiments, we assess the quality of learned representations via linear evaluation and fine-tuning across downstream tasks, including multi-disease classification and diabetic retinopathy grading. Our results show that tailoring augmentation strategies to the characteristics of retinal images plays a critical role in improving performance. Even under constrained settings, lightweight SSL frameworks can learn transferable representations that reduce dependence on large annotated datasets and achieve competitive results.
