EyeCLIP: A visual-language foundation model for multi-modal ophthalmic image analysis
<|reference_start|>EyeCLIP: A visual-language foundation model for multi-modal ophthalmic image analysis: Early detection of eye diseases like glaucoma, macular degeneration, and diabetic retinopathy is crucial for preventing vision loss. While artificial intelligence (AI) foundation models hold significant promise for addressing these challenges, existing ophthalmic foundation models primarily focus on a single modality, whereas diagnosing eye diseases requires multiple modalities. A critical yet often overlooked aspect is harnessing the multi-view information across various modalities for the same patient. Additionally, due to the long-tail nature of ophthalmic diseases, standard fully supervised or unsupervised learning approaches often struggle. Therefore, it is essential to integrate clinical text to capture a broader spectrum of diseases. We propose EyeCLIP, a visual-language foundation model developed using over 2.77 million multi-modal ophthalmology images with partial text data. To fully leverage the large multi-modal unlabeled and labeled data, we introduced a pretraining strategy that combines self-supervised reconstructions, multi-modal image contrastive learning, and image-text contrastive learning to learn a shared representation of multiple modalities. Through evaluation using 14 benchmark datasets, EyeCLIP can be transferred to a wide range of downstream tasks involving ocular and systemic diseases, achieving state-of-the-art performance in disease classification, visual question answering, and cross-modal retrieval. EyeCLIP represents a significant advancement over previous methods, especially showcasing few-shot, even zero-shot capabilities in real-world long-tail scenarios.<|reference_end|>
@article{shi2024eyeclip:,
    title={EyeCLIP: A visual-language foundation model for multi-modal ophthalmic
  image analysis},
    author={Danli Shi, Weiyi Zhang, Jiancheng Yang, Siyu Huang, Xiaolan Chen,
  Mayinuer Yusufu, Kai Jin, Shan Lin, Shunming Liu, Qing Zhang, Mingguang He},
    journal={arXiv preprint arXiv:2409.06644},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.06644},
    primaryClass={cs.CV cs.AI}
}