← 研踪 PaperTrailarXiv:2608.26686 · v2
Domain-Specific Self-Supervised Representation Learning for Retinal Fundus Classification
首次提交 2026-08-27 14:44
版本更新 2026-10-28 17:12
雷达观测 2026-10-30 10:22
摘要
Annotated medical images remain scarce and costly to obtain, which motivates self-supervised learning (SSL). We present a controlled empirical study of two SSL frameworks, SimSiam and SimCLR, for retinal fundus classification, asking whether retina-specific augmentations (Graham's local-mean subtraction and CLAHE) improve representations beyond standard augmentation and grayscale conversion. ResNet-18 backbones are pretrained on 48K unlabeled fundus images and evaluated by linear probing and fine-tuning on EDID (multi-disease) and RetinaMNIST (DR grading). On EDID, grayscale conversion accounts for most of SimSiam's linear-probe gain, and Graham or CLAHE add little on top of it. After fine-tuning, SimSiam with Graham+Gray exceeds an ImageNet-supervised baseline on RetinaMNIST (+3.0 accuracy points) but only matches it on EDID. Retina-specific augmentation therefore helps inconsistently and should be validated per SSL method and task.
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v2 · 2026-10-28 17:12 · 最新观测版本
Domain-Specific Self-Supervised Representation Learning for Retinal Fundus Classification
Annotated medical images remain scarce and costly to obtain, which motivates self-supervised learning (SSL). We present a controlled empirical study of two SSL frameworks, SimSiam and SimCLR, for retinal fundus classification, asking whether retina-specific augmentations (Graham's local-mean subtraction and CLAHE) improve representations beyond standard augmentation and grayscale conversion. ResNet-18 backbones are pretrained on 48K unlabeled fundus images and evaluated by linear probing and fine-tuning on EDID (multi-disease) and RetinaMNIST (DR grading). On EDID, grayscale conversion accounts for most of SimSiam's linear-probe gain, and Graham or CLAHE add little on top of it. After fine-tuning, SimSiam with Graham+Gray exceeds an ImageNet-supervised baseline on RetinaMNIST (+3.0 accuracy points) but only matches it on EDID. Retina-specific augmentation therefore helps inconsistently and should be validated per SSL method and task.
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