arXiv AI 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

EyeMVP: OCT-Informed Fundus Representation Learning via Paired CFP--OCT Pretraining

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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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv AI
Sep 1

Co-Annotator: Expert-Distilled ViT and VLM for Visual and Documentation Guidance in Age-Related Macular Degeneration

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By Ziheng "Leo" Li, Benjamin Freeman, Akshay Raman, Kavin Aravindhan Rajkumar, Xinxin Fang, Rishabh Srivastava, Steven Feiner, Kaveri A. Thakoor
arXiv Machine Learning
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Counterfactual Explainability Framework With CycleGAN And Counterfactual-Classifier Alignnment Score for Retinal Disease Classification

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 AI
Sep 10

Clinician-Friendly Foundation Models for Ophthalmic Image Diagnostics without Fine-Tuning or Technical Barriers

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