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. Results indicate that tailored augmentations enable lightweight SSL models to learn transferable representations, reducing reliance on large annotated datasets while achieving competitive performance.
By Bekzat Nurlanbekova, Fung Fung Ting
The paper reports a controlled study of self‑supervised learning (SSL) objectives for image and video pretraining under limited data, architecture, and compute budgets. It compares contrastive, reconstruction, feature‑prediction, and diffusion methods, finding that DINOv2‑style pretraining delivers the best overall performance. Combining DINOv2 with video SSL objectives such as VideoMAE improves image classification and segmentation but harms video tracking and camera‑pose estimation, highlighting a trade‑off between semantic and geometric learning.
By Brun\'o B. Englert, Gijs Dubbelman
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:2605. 23995v2 Announce Type: replace-cross Abstract: Self-supervised learning (SSL) has emerged as a promising paradigm for addressing the annotation bottleneck in medical imaging by learning representations from unlabeled data.
By Chathura Wimalasiri
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.
By Chathura Wimalasiri, Kishor Nandakishor, Marimuthu Palaniswami
The paper investigates the effect of using semantic positive pairs—different instances of the same class—in self‑supervised visual representation learning. By creating matched ImageNet‑1K subsets of augmented pairs and manually curated semantic pairs, the authors compare contrastive and non‑contrastive SSL methods under identical training conditions. Across transfer learning and object detection tasks, semantic‑pair pretraining consistently outperforms augmented‑pair pretraining, with contrastive methods like SimCLR showing the largest gains, indicating that semantic pairs foster additional invariances beyond standard augmentations.
By Mohammad Alkhalefi, Georgios Leontidis, Mingjun Zhong