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This study introduces GlobeReady, a foundation model designed to address the critical scalability issue of AI in medical imaging by eliminating the need for retraining across different clinical settings. By leveraging self-supervised learning on synthetic data and contrastive learning on diverse real-world datasets, the platform demonstrates robust domain generalizability and usability among ophthalmologists without requiring technical barriers or fine-tuning. The research highlights a significant advancement in deploying large-scale AI models for specific medical imaging tasks like color fundus photographs and OCT scans globally.
Clinician-Friendly Foundation Models for Ophthalmic Image Diagnostics without Fine-Tuning or Technical Barriers
arXiv:2504.15928v3 Announce Type: replace-cross Abstract: Artificial intelligence (AI) shows remarkable potential in medical imaging diagnostics, yet most current models require retraining when applied across different clinical settings, limiting their scalability. We developed GlobeReady, a deployment-oriented platform powered by the RetiGlobe foun- dation model and local feature augmentation. RetiGlobe was pretrained in two stages: 1) self-supervised learning using DINOv2 on 38 million synthetic ophthalmic images, and 2) contrastive learning using CLIP on 475,845 real image-text pairs spanning diverse ethnicities, imaging devices, and geographic regions worldwide. We evaluate GlobeReady on 488,448 ophthalmic images, including color fundus photographs (CFPs) and optical coherence tomography scans, from multi-centres in China, Singapore, Vietnam and the UK. Prospective testing included usability assessment with 31 ophthalmologists. Exploratory analyses evaluated domain generalisability, Bayesian uncertainty quantification, out-of-distribution (OOD) detection, and feature-based case retrieval.
Authors: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
Abstract:Artificial intelligence (AI) shows remarkable potential in medical imaging diagnostics, yet most current models require retraining when applied across different clinical settings, limiting their scalability. We developed GlobeReady, a deployment-oriented platform powered by the RetiGlobe foun- dation model and local feature augmentation. RetiGlobe was pretrained in two stages: 1) self-supervised learning using DINOv2 on 38 million synthetic ophthalmic images, and 2) contrastive learning using CLIP on 475,845 real image-text pairs spanning diverse ethnicities, imaging devices, and geographic regions worldwide. We evaluate GlobeReady on 488,448 ophthalmic images, including color fundus photographs (CFPs) and optical coherence tomography scans, from multi-centres in China, Singapore, Vietnam and the UK. Prospective testing included usability assessment with 31 ophthalmologists. Exploratory analyses evaluated domain generalisability, Bayesian uncertainty quantification, out-of-distribution (OOD) detection, and feature-based case retrieval.
Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
arXiv:2504.15928 [cs.CV]
(or arXiv:2504.15928v3 [cs.CV] for this version)
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