Summary of Eyeclip: a Visual-language Foundation Model For Multi-modal Ophthalmic Image Analysis, by Danli Shi et al.
EyeCLIP: A visual-language foundation model for multi-modal ophthalmic image analysis
by Danli Shi, Weiyi Zhang, Jiancheng Yang, Siyu Huang, Xiaolan Chen, Mayinuer Yusufu, Kai Jin, Shan Lin, Shunming Liu, Qing Zhang, Mingguang He
First submitted to arxiv on: 10 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A new foundation model for diagnosing eye diseases like glaucoma, macular degeneration, and diabetic retinopathy is proposed, leveraging multi-view information from various modalities. The EyeCLIP model is trained on over 2.77 million images with partial text data, using a pretraining strategy that combines self-supervised reconstructions, contrastive learning, and image-text contrastive learning to learn a shared representation of multiple modalities. This approach enables state-of-the-art performance in disease classification, visual question answering, and cross-modal retrieval on 14 benchmark datasets. The model can be transferred to various downstream tasks involving ocular and systemic diseases, showcasing few-shot and even zero-shot capabilities in real-world scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new computer program helps doctors diagnose eye problems like glaucoma and diabetes. This program uses many different images of eyes, along with some text about the patients, to learn how to recognize signs of disease. It’s better than other programs at identifying diseases, even when it only has a few examples to work from. This could be very helpful for doctors who need to diagnose eye problems in people all around the world. |
Keywords
» Artificial intelligence » Classification » Few shot » Pretraining » Question answering » Self supervised » Zero shot
