arXiv Computer Vision
Self-supervised Pre-training Helps Retinal Disease Progression Modelling Most When Data Is Scarce
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Towards Interpretable Foundation Models for Retinal Fundus Images
arXiv:2603. 18846v3 Announce Type: replace-cross Abstract: Foundation models are used to extract transferable representations from large amounts of unlabeled data, typically via self-supervised learning (SSL).
arXiv AI
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Diagnostic-Guided Longitudinal Modeling for Forecasting Retinal Atrophy Progression
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arXiv AI
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Task-Aligned Self-Supervised Learning for Medical Image Analysis: A Task-Oriented Review with Practical Design Guidelines
arXiv:2605. 23995v4 Announce Type: replace-cross Abstract: Self-supervised learning (SSL) is increasingly used in medical image analysis to reduce dependence on costly expert annotations by learning transferable representations from unlabeled data.
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A Framework for Early Sepsis Prediction via Self-Supervised (JEPA) and Federated Representation Learning
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Hugging Face Trending Papers
Aug 27
Domain-Specific Self-Supervised Representation Learning for Retinal Fundus Classification
The paper explores contrastive self‑supervised learning (SSL) for retinal fundus image classification, comparing SimSiam and SimCLR under limited data and computational resources. It investigates how retinal‑specific augmentation strategies and training parameters affect representation quality, evaluated through linear probing and fine‑tuning on multi‑disease classification and diabetic retinopathy grading tasks. The results demonstrate that tailored augmentations enable lightweight SSL models to learn transferable representations, reducing reliance on large annotated datasets while achieving competitive performance.
arXiv AI
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Self-supervision drives representational convergence in medical foundation models more than clinical supervision
arXiv:2607. 20274v1 Announce Type: cross Abstract: Medical image encoders from different groups are increasingly treated as interchangeable, on the assumption that scale and clinical supervision concentrate their representations onto a shared structure.