bibtext: @InProceedings{ Wu_MMRetinal_MICCAI2024,
                   author = { Wu, Ruiqi and Zhang, Chenran and Zhang, Jianle and Zhou, Yi and Zhou, Tao and Fu, Huazhu },
                   title = { { MM-Retinal: Knowledge-Enhanced Foundational Pretraining with Fundus Image-Text Expertise } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15001 },
                   month = {October},
                   pages = { pending },
                }
abstract: Current fundus image analysis models are predominantly built for specific tasks relying on individual datasets. The learning process is usually based on data-driven paradigm without prior knowledge. To address this issue, we propose MM-Retinal, a multi-modal dataset that encompasses high-quality image-text pairs collected from professional fundus diagram books. Moreover, enabled by MM-Retinal, we present a novel Knowledge-enhanced foundational pretraining model which incorporates Fundus Image-Text expertise, called KeepFIT. It is designed with image similarity-guided text revision and mixed training strategy to infuse expert knowledge. Our proposed fundus foundation model achieves state-of-the-art performance across six unseen downstream tasks and holds excellent generalization ability in zero-shot and few-shot scenarios. MM-Retinal and KeepFIT are available at \href{https://github.com/lxirich/MM-Retinal}{here}
title: MM-Retinal: Knowledge-Enhanced Foundational Pretraining with Fundus Image-Text Expertise
authors: Wu, Ruiqi Zhang, Chenran Zhang, Jianle Zhou, Yi Zhou, Tao Fu, Huazhu