woma: a real-time foundation model and its fine-tuned models for endoscopy

arXiv:2609.15130 · cs.SE, cs.CV, cs.LG · Submitted 2026-09-14 · Read on arXiv

cs.SE, cs.CV, cs.LG

Submitted: 2026-09-14

Updated: 2026-09-14

Comments: 28 pages, 10 figures (8 in the main text, 2 supplementary), 13 tables (8 in the main text, 5 supplementary). Preprint. Models and run records are available from the corresponding author

Code: https://github.com/ultralytics/ultralytics

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Foundation Model — 76× in this paper · explained in 3 episodes
• Accuracy — 31× in this paper · explained in 1 episode
• Fine-Tuning — 10× in this paper · explained in 5 episodes
• Transformer — 6× in this paper · explained in 2 episodes
• Benchmark — 5× in this paper · explained in 2 episodes
• Distillation — 5× in this paper · explained in 2 episodes
• Adaptability — 4× in this paper · explained in 1 episode
• Computer Vision and Pattern Recognition — 4× in this paper
• Decode — 4× in this paper · explained in 2 episodes
• Adam — 2× in this paper · explained in 2 episodes
• Core — 2× in this paper · explained in 2 episodes
• Foundation Models — 2× in this paper · explained in 7 episodes
• Regularization — 2× in this paper · explained in 3 episodes
• Self-Supervised Learning — 2× in this paper · explained in 7 episodes
• Adaptation — 1× in this paper · explained in 1 episode
• Allocator — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 3 episodes
• Calibration — 1× in this paper · explained in 7 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Hallucination — 1× in this paper · explained in 3 episodes
• In International Conference on Learning Representations — 1× in this paper
• International Conference on Machine Learning — 1× in this paper
• Multimodal Learning — 1× in this paper · explained in 2 episodes
• Segmentation Masks — 1× in this paper · explained in 2 episodes
• Self-distillation — 1× in this paper · explained in 6 episodes

Sources

• Numbat: Building and Verifying a Self-Contained Machine-Learning Stack
• Emerging Properties in Self-Supervised Vision Transformers
• DINOv2: Learning Robust Visual Features without Supervision
• Masked Autoencoders Are Scalable Vision Learners
• Designing BERT for Convolutional Networks: Sparse and Hierarchical Masked Modeling
• ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders
• VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning
• Self-Supervised Pretraining Improves Self-Supervised Pretraining
• Making Convolutional Networks Shift-Invariant Again
• Endoscopy disease detection challenge 2020
• GastroVision: A Multi-class Endoscopy Image Dataset for Computer Aided Gastrointestinal Disease Detection
• SAGE: An Expert-Annotated South Asian GI Endoscopy Dataset for Multimodal Learning and Hallucination Analysis
• Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detection
• Decoupled Weight Decay Regularization
• GIM-ENDO: A Multimodal Endoscopic Image and Video Dataset for Gastric Intestinal Metaplasia Morphology and Pathology

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