MM-Retinal: Knowledge-Enhanced Foundational Pretraining with Fundus Image-Text Expertise
<|reference_start|>MM-Retinal: Knowledge-Enhanced Foundational Pretraining with Fundus Image-Text Expertise: 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, resulting in poor transferability and generalizability. 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 https://github.com/lxirich/MM-Retinal.<|reference_end|>
@article{wu2024mm-retinal:,
    title={MM-Retinal: Knowledge-Enhanced Foundational Pretraining with Fundus
  Image-Text Expertise},
    author={Ruiqi Wu, Chenran Zhang, Jianle Zhang, Yi Zhou, Tao Zhou, Huazhu Fu},
    journal={arXiv preprint arXiv:2405.11793},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.11793},
    primaryClass={cs.CV}
}