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<p>Early detection of eye diseases is vital for preventing vision loss. Existing ophthalmic artificial intelligence models focus on single modalities, overlooking multi-view information and struggling with rare diseases due to long-tail distributions. We propose EyeCLIP, a multimodal visual-language foundation model trained on 2.77 million ophthalmology images from 11 modalities with partial clinical text. Our novel pretraining strategy combines self-supervised reconstruction, multimodal image contrastive learning, and image-text contrastive learning to capture shared representations across modalities. EyeCLIP demonstrates robust performance across 14 benchmark datasets, excelling in disease classification, visual question answering, and cross-modal retrieval. It also exhibits strong few-shot and zero-shot capabilities, enabling accurate predictions in real-world, long-tail scenarios. EyeCLIP offers significant potential for detecting both ocular and systemic diseases, and bridging gaps in real-world clinical applications.</p>

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A multimodal visual–language foundation model for computational ophthalmology

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年6月21日

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  • Danli Shi,
  • Weiyi Zhang,
  • Jiancheng Yang,
  • Siyu Huang,
  • Xiaolan Chen,
  • Pusheng Xu,
  • Kai Jin,
  • Shan Lin,
  • Jin Wei,
  • Mayinuer Yusufu,
  • Shunming Liu,
  • Qing Zhang,
  • Zongyuan Ge,
  • Xun Xu,
  • Mingguang He

摘要

Early detection of eye diseases is vital for preventing vision loss. Existing ophthalmic artificial intelligence models focus on single modalities, overlooking multi-view information and struggling with rare diseases due to long-tail distributions. We propose EyeCLIP, a multimodal visual-language foundation model trained on 2.77 million ophthalmology images from 11 modalities with partial clinical text. Our novel pretraining strategy combines self-supervised reconstruction, multimodal image contrastive learning, and image-text contrastive learning to capture shared representations across modalities. EyeCLIP demonstrates robust performance across 14 benchmark datasets, excelling in disease classification, visual question answering, and cross-modal retrieval. It also exhibits strong few-shot and zero-shot capabilities, enabling accurate predictions in real-world, long-tail scenarios. EyeCLIP offers significant potential for detecting both ocular and systemic diseases, and bridging gaps in real-world clinical applications.