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.
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
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
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:2607. 04673v1 Announce Type: cross Abstract: Glaucoma is a leading cause of irreversible blindness worldwide, yet most automated diagnosis systems rely on opaque deep-learning models that offer little clinical interpretability.
By Cheng Huang, Jia Zhang, Yi Jiang, Yang Liu, Karanjit Kooner, Yadi Liu, Tsengdar Lee, Yang Xie, Wenqi Shi, Guanghua Xiao
arXiv:2508. 03750v2 Announce Type: replace Abstract: Early and accurate glaucoma detection is critical to prevent irreversible vision loss, yet existing AI methods often rely on unimodal inputs and lack interpretability.
By Cheng Huang, Zeyu Han, Weizheng Xie, Karanjit Kooner, Tsengdar Lee, Jui-Kai Wang, Jia Zhang
CAMEL (Cross-Architecture Masked Embedding Learning) is a new distillation framework that transfers knowledge from large foundation models to lightweight student models using an asymmetric masking strategy during embedding learning. The method is designed for retinal image analysis, where existing foundation models like RETFound excel but are computationally heavy. Experiments on four datasets demonstrate that CAMEL maintains accuracy while dramatically reducing computational complexity, making it suitable for resource‑limited clinical settings.
By Andre Guarnier De Mitri, Giovani Decico Lucafo, Miller Matheus Lima Anacleto Rocha, Diego Furtado Silva
OptiModNet is a lightweight UNet‑Transformer hybrid designed for optic disc and cup segmentation. It incorporates grouped‑query and channel attention across multiple stages, along with an Aggregated Pyramid Loss to improve gradient flow and structural consistency. Evaluated on the REFUGE2 dataset, it surpasses existing methods by over 2.5 % while using only 3.73 GFLOPs and 1.93 M parameters.
By Soumili Ghosh, Debapriya Roy, Aryan Das, Bikash Santra
arXiv:2609.06699v1 Announce Type: cross
Abstract: Glaucoma is a progressive optic neuropathy characterized by irreversible damage to the optic nerve, making timely diagnosis critical to prevent perma...
By Abdullah Al Shafi, Nishat Sadaf Lira, Abrar Hasan, Kazi Saeed Alam, Swapnil Kundu Argha
The paper introduces FOCUS, a cross‑dataset benchmark for retinal fundus models that evaluates vision‑only encoders, vision‑language dual encoders, and multimodal large language models across ten public datasets. It measures ranking performance, calibration, subgroup disparities, and image‑quality robustness for tasks such as diabetic retinopathy and glaucomatous optic neuropathy. Results show no single model family dominates, with vision‑only encoders ranking highest on average, while fine‑tuning improves in‑domain performance but varies in external transfer.
By David Restrepo, Chenwei Wu, Luis Filipe Nakayama, Miguel L. Martins, Stergios Christodoulidis, Maria Vakalopoulou, Enzo Ferrante
The paper presents an explainable diabetic retinopathy classification framework that leverages vision foundation models—DINOv2, CLIP, and Vision Transformer—combined with various transfer learning techniques such as full fine‑tuning, linear probing, and Low‑Rank Adaptation (LoRA). Using the ODIR dataset for internal validation and the APTOS dataset for external testing, DINOv2‑LoRA achieved the best internal AUROC (0.758) while DINOv2 and ViT full fine‑tuning reached the highest external AUROC (0.920). Explainability was assessed with Grad‑CAM and HiResCAM against expert‑annotated lesion masks from IDRiD, using Dice, IoU, and Pointing Game metrics, confirming that model attention aligns with clinically relevant retinal lesions.
By Abhishek Verma, Anila Krishna, Abhishek Gajanan Bankar, Juan Miguel Lopez Alcaraz
The paper introduces GlobeReady, a clinician-friendly platform that leverages the RetiGlobe foundation model for ophthalmic image diagnostics without requiring fine-tuning. RetiGlobe was pretrained in two stages: first with self-supervised learning on 38 million synthetic images, then with contrastive learning on 475,845 real image‑text pairs from diverse ethnicities, devices, and regions. GlobeReady was evaluated on 488,448 images from multiple international centers and tested prospectively with 31 ophthalmologists, also exploring domain generalisability, uncertainty quantification, OOD detection, and feature-based case retrieval.
By Meng Wang, Tian Lin, Qingshan Hou, Aidi Lin, Lianyu Wang, Jingcheng Wang, Qingsheng Peng, Truong X. Nguyen, Zhi Da Soh, Xiayin Zhang, Jingyan Yang, Danqi Fang, Ke Zou, Ting Xu, Can Can Xue, Ten Cheer Quek, Qinkai Yu, Minxin Liu, Hui Zhou, Zixuan Xiao, Guiqin He, Huiyu Liang, Tingkun Shi, Man Chen, Zhuangling Lin, Linna Liu, Yuanyuan Peng, Li Jia Chen, Chi Ming Chan, Xiaohong Li, Junren He, Zhirong Xu, Tingbing Fang, Yanli Wang, Qingzhi Wang, Wenyi Hu, Yujie Wang, Li Li, Jiaying Ye, Tonghui Ye, Liang Lyu, Yongjian Lu, Ruoshi Cai, Yiwen Tang, Qiuming Hu, Junhong Chen, Zhenhua Zhang, Cheng Chen, Yitian Zhao, Dianbo Liu, Jianhua Wu, Xinjian Chen, Changqing Zhang, Xiaojun Wu, Triet Thanh Nguyen, Yanda Meng, Yalin Zheng, Daoqiang Zhang, Xiaochun Cao, Yih Chung Tham, Ye Zhang, Ying Han, Alvin L Young, Mary Ho, Carmen K M Chan, Clement C Tham, Zhuoting Zhu, Carol Y. Cheung, Tien Yin Wong, Huazhu Fu, Haoyu Chen, Ching-Yu Cheng