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: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).
By Samuel Ofosu Mensah, Camila Roa, Kerol Djoumessi, Philipp Berens
Self-supervision is a powerful technique for learning visual representations from unlabeled data. Existing techniques primarily adopt a two-stage approach for self-supervised learning (SSL): a pretraining stage on unlabeled data followed by a finetuning stage on labeled data.
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 study evaluates fundus-specific foundation models (FM) for detecting diabetic macular edema (DME) in retinal images. It compares two popular FM—RETFound and FLAIR—against a lightweight EfficientNet-B0 backbone across multiple datasets (IDRiD, MESSIDOR-2, and OCT-and-Eye-FundusImages). Results indicate that FM do not consistently outperform fine‑tuned CNNs; EfficientNet-B0 often matches or exceeds FM performance, with FLAIR being the most competitive FM.
By Franco Javier Arellano, Jos\'e Ignacio Orlando
arXiv:2512. 10244v2 Announce Type: replace-cross Abstract: Semi-supervised few-shot learning (SSFSL) resembles real-world applications such as auto-annotation, as it aims to learn a model from a few labeled and abundant unlabeled task-specific examples to annotate the unlabeled ones.
By Tian Liu, Anwesha Basu, James Caverlee, Shu Kong
arXiv:2608. 03218v1 Announce Type: cross Abstract: Dataset distillation compresses a large training set into a compact synthetic set while retaining its downstream utility.
By Mingzhuo Li, Guang Li, Linfeng Ye, Jiafeng Mao, Takahiro Ogawa, Konstantinos N. Plataniotis, Miki Haseyama
arXiv:2608.24723v1 Announce Type: new
Abstract: Retinal fundus photography is widely used for screening and monitoring ocular diseases, but many modern classification pipelines rely on deep latent re...
By Xiaoyan Li, Shixin Xu, Arvind Gupta, Huaxiong Huang
arXiv:2609.12834v1 Announce Type: new
Abstract: Modelling how a disease progresses over time requires longitudinal imaging cohorts, which are scarce and small, whereas cross-sectional data -- one ima...
By Ifeoma Veronica Nwabufo, Julius Gervelmeyer, Sarah M\"uller, Philipp Berens
Dataset distillation compresses a large training set into a compact synthetic set while retaining its downstream utility. Most existing methods target randomly initialized networks, whereas modern vision systems often adapt frozen pretrained encoders with lightweight modules.
Retinal fundus photography is widely used for screening and monitoring ocular diseases, but many modern classification pipelines rely on deep latent representations and provide limited interpretabilit...