# EndoDINO: A Foundation Model for GI Endoscopy

arXiv: https://arxiv.org/abs/2501.05488 · HTML: https://mlsanity.com/paper/2501.05488/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jan 2025.

## Built on by (most cited first, 9 of 9)

- [PolypSegTrack: Unified Foundation Model for Colonoscopy Video Analysis](https://mlsanity.com/paper/2503.24108/) (2025)
- [EndoCogniAgent: Closed-Loop Agentic Reasoning with Self-Consistency Validation for Endoscopic Diagnosis](https://mlsanity.com/paper/2508.07292/) (2025)
- [Federated Foundation Model for GI Endoscopy Images](https://mlsanity.com/paper/2505.24108/) (2025)
- [CLoE: Curriculum Learning on Endoscopic Images for Robust MES Classification](https://mlsanity.com/paper/2508.13280/) (2025)
- [MEGAN: Mixture of Experts for Robust Uncertainty Estimation in Endoscopy Videos](https://mlsanity.com/paper/2509.12772/) (2025)
- [DeepGI: Explainable Deep Learning for Gastrointestinal Image Classification](https://mlsanity.com/paper/2511.21959/) (2025)
- [Analogical Reasoning as a Doctor: A Foundation Model for Gastrointestinal Endoscopy Diagnosis](https://mlsanity.com/paper/2604.05649/) (2026)
- [Geometry-Consistent Endoscopic Representations for Image-Guided Navigation via Structured Foundation Model Adaptation](https://mlsanity.com/paper/2606.17340/) (2026)
- [EndoVLM: An Endoscopy Vision-Language Pre-training Model via Anatomy-Guided Sparsity and Progressive Alignment](https://mlsanity.com/paper/2608.04472/) (2026)

## Abstract (arXiv)

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.