A Surgical Foundation Model Reveals Task-Dependent Label Efficiency

arXiv:2609.31821 · cs.CV, cs.AI · Submitted 2026-09-25 · Read on arXiv

cs.CV, cs.AI

Submitted: 2026-09-25

Updated: 2026-09-25

Code: https://github.com/CAMMA-public/SURGE

Terminology

• Foundation Model — 20× in this paper · explained in 3 episodes
• Benchmark — 13× in this paper · explained in 2 episodes
• Adaptation — 12× in this paper · explained in 1 episode
• Transformer — 12× in this paper · explained in 2 episodes
• Critical View of Safety — 9× in this paper
• Foundation Models — 9× in this paper · explained in 7 episodes
• Linear Probing — 6× in this paper · explained in 2 episodes
• Fine-Tuning — 4× in this paper · explained in 5 episodes
• Generalization — 4× in this paper · explained in 13 episodes
• Representation Learning — 4× in this paper · explained in 2 episodes
• Self-Supervised Learning — 4× in this paper · explained in 8 episodes
• Consistency — 3× in this paper · explained in 3 episodes
• Downstream Tasks — 3× in this paper · explained in 2 episodes
• Regularization — 3× in this paper · explained in 3 episodes
• AUC — 2× in this paper · explained in 2 episodes
• Embeddings — 2× in this paper · explained in 2 episodes
• F1 Score — 2× in this paper · explained in 3 episodes
• Robustness — 2× in this paper · explained in 5 episodes
• Abstraction — 1× in this paper · explained in 1 episode
• Aggregation — 1× in this paper · explained in 1 episode
• Alignment — 1× in this paper · explained in 1 episode
• Archetype — 1× in this paper · explained in 1 episode
• Attention Mechanism — 1× in this paper · explained in 3 episodes
• Contextual Understanding — 1× in this paper · explained in 2 episodes
• Contrastive Learning — 1× in this paper · explained in 5 episodes
• Distillation — 1× in this paper · explained in 2 episodes
• Domain Adaptation — 1× in this paper · explained in 5 episodes
• Explainability — 1× in this paper · explained in 3 episodes
• Grounding — 1× in this paper · explained in 3 episodes
• Semantic Understanding — 1× in this paper · explained in 2 episodes
• Temporal Reasoning — 1× in this paper · explained in 2 episodes
• Transferability — 1× in this paper · explained in 4 episodes
• Transformer Architecture — 1× in this paper · explained in 2 episodes
• Vision Transformer (ViT) — 1× in this paper · explained in 2 episodes

Sources

• The Endoscapes Dataset for Surgical Scene Segmentation, Object Detection, and Critical View of Safety Assessment: Official Splits and Benchmark
• 2017 Robotic Instrument Segmentation Challenge
• SurgiSAM2: Fine-tuning a foundational model for surgical video anatomy segmentation and detection
• Surgical Anatomy Recognition with Context Learning using Foundation Representations
• ProstaTD: Bridging Surgical Triplet from Classification to Fully Supervised Detection
• Mugs: A Multi-Granular Self-Supervised Learning Framework
• iBOT: Image BERT Pre-Training with Online Tokenizer
• DINOv3
• CliPPER: Contextual Video-Language Pretraining on Long-form Intraoperative Surgical Procedures for Event Recognition
• General surgery vision transformer: A video pre-trained foundation model for general surgery
• Robust Medical Instrument Segmentation Challenge 2019
• SAR-RARP50: Segmentation of surgical instrumentation and Action Recognition on Robot-Assisted Radical Prostatectomy Challenge
• Surgical Visual Understanding (SurgVU) Dataset
• The SARAS Endoscopic Surgeon Action Detection (ESAD) dataset: Challenges and methods
• The SAGES Critical View of Safety Challenge: A Global Benchmark for AI-Assisted Surgical Quality Assessment
• An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
• RoFormer: Enhanced Transformer with Rotary Position Embedding
• Spreading vectors for similarity search

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