EndoDINO: A Foundation Model for GI Endoscopy
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915206059261952 |
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| author | Dermyer, Patrick Kalra, Angad Schwartz, Matt |
| author_facet | Dermyer, Patrick Kalra, Angad Schwartz, Matt |
| contents | In this work, we present EndoDINO, a foundation model for GI endoscopy tasks that achieves strong generalizability by pre-training on a well-curated image dataset sampled from the largest known GI endoscopy video dataset in the literature. Specifically, we pre-trained ViT models with 1B, 307M, and 86M parameters using datasets ranging from 100K to 10M curated images. Using EndoDINO as a frozen feature encoder, we achieved state-of-the-art performance in anatomical landmark classification, polyp segmentation, and Mayo endoscopic scoring (MES) for ulcerative colitis with only simple decoder heads. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_05488 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | EndoDINO: A Foundation Model for GI Endoscopy Dermyer, Patrick Kalra, Angad Schwartz, Matt Computer Vision and Pattern Recognition Image and Video Processing In this work, we present EndoDINO, a foundation model for GI endoscopy tasks that achieves strong generalizability by pre-training on a well-curated image dataset sampled from the largest known GI endoscopy video dataset in the literature. Specifically, we pre-trained ViT models with 1B, 307M, and 86M parameters using datasets ranging from 100K to 10M curated images. Using EndoDINO as a frozen feature encoder, we achieved state-of-the-art performance in anatomical landmark classification, polyp segmentation, and Mayo endoscopic scoring (MES) for ulcerative colitis with only simple decoder heads. |
| title | EndoDINO: A Foundation Model for GI Endoscopy |
| topic | Computer Vision and Pattern Recognition Image and Video Processing |
| url | https://arxiv.org/abs/2501.05488 |