The study presents an interpretable AI framework for glaucoma screening using low-cost handheld retinal cameras in a West African population. Trained on 681 participants, the system achieved high performance across vessel segmentation, cup/disc segmentation, and optic nerve head feature detection, with classification AUCs of 0.85 for the handheld device and 0.93 for a tabletop camera. The model provides confidence scores and visual explanations to aid clinical interpretation.
By Charis Y. N. Chiang, Tarela Sarimiye, Adeyinka Ashaye, Martin Buist, Michael A. Hauser, Olusola Olawoye, Micha\"el J. A. Girard
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
arXiv:2607. 21068v1 Announce Type: new Abstract: Automated detection of vision impairing retina-based ocular conditions from fundus images is important for early screening, timely referral and reducing dependency on specialist-only assessment, for which neural network-based deep learning (DL) models have been widely utilized.
By Kritanu Chattopadhyay, Sayanjit Singha Roy, Soumya Chatterjee
arXiv:2607. 19864v1 Announce Type: cross Abstract: Diabetic retinopathy is a leading cause of preventable blindness; its early lesions are small, low contrast, and easily missed in manual screening.
By Z\"ubeyr \"Ozeren, Tansel Uyar
The paper presents a two‑pipeline framework for retinal fundus analysis that combines four‑class disease classification with vessel segmentation. It fine‑tunes eight ImageNet‑pretrained CNNs on the FIVES dataset, applies five gradient‑based explanation methods to assess model interpretability, and benchmarks ten U‑Net variants—including transformer‑based and attention‑enhanced architectures—on the FIVES and DRIVE datasets. The best classification results come from ResNet101 (94.17% accuracy), while the strongest segmentation performance is achieved by Attention U‑Net with a ResNet101V2 backbone, improving DRIVE IoU from 60.80% to 64.83%.
By Fatema Tuj Johora Faria, Mukaffi Bin Moin, Pronay Debnath, Asif Iftekher Fahim, Faisal Muhammad Shah
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
arXiv:2607. 03959v1 Announce Type: cross Abstract: Diabetic retinopathy (DR) is a leading cause of vision impairment worldwide, highlighting the need for accurate and accessible screening tools.
By Rashadul Hasan Badhon, Atalie Carina Thompson, Jennifer I. Lim, Theodore Leng, Minhaj Nur Alam
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...
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:2509.25549v3 Announce Type: replace-cross
Abstract: Choroidal nevi are common benign pigmented lesions in the eye, with a small risk of transforming into melanoma. Early detection is critical t...
By Mohammadmahdi Eshragh, Emad A. Mohammed, Behrouz Far, Ezekiel Weis, Carol L Shields, Sandor R Ferenczy, Trafford Crump
The study investigates how the choice of annotation used to set a segmentation threshold influences retinal vessel segmentation performance. Using all 28 CHASE DB1 images and two human observers, the authors fit random forests and Extra Trees models, then compare five threshold policies—including fixed, observer‑tuned, mean‑observer, and maximin tuning—on the same score maps. Results show that maximin tuning alters thresholds in most fits but yields negligible changes in worst‑observer Dice scores, suggesting no accuracy advantage in this cohort.
By Wenhao Xu, Yixian Kong, Ting Pan, Changwei Wang, Feilong Wang, Rongtao Xu
arXiv:2606. 15129v1 Announce Type: cross Abstract: Color fundus photography (CFP) is the mainstay for large-scale retinal screening, yet its diagnostic capacity is constrained by the lack of depth-resolved structural information.
By Zhuo Deng, Ruiheng Zhang, Ziheng Zhang, Weihao Gao, Yitong Li, Qian Wang, Lei Shao, Jiaoyue Dong, Zhixi Zeng, Lijian Fang, Haibo Wang, Xiaobin Lin, Tao Liu, Zhicheng Du, Zhengwei Zhang, Lin Yang, Zheng Gong, Xinyu Zhao, Zhenquan Wu, Fang Li, Zhiguang Zhou, Guoming Zhang, Sun Jing, Han Lv, Wenbin We, Lan Ma