A Surgical Foundation Model Reveals Task-Dependent Label Efficiency

Florian Philipp Stilz; Lorenzo Arboit; Vinkle Srivastav; CAMMA International Surgical Partners; Jacques Marescaux; Sergio Alfieri; Pietro Mascagni; Nassir Navab; Nicolas Padoy

arXiv · 2609.31821

A Surgical Foundation Model Reveals Task-Dependent Label Efficiency

Florian Philipp Stilz·Lorenzo Arboit·Vinkle Srivastav·CAMMA International Surgical Partners·Jacques Marescaux·Sergio Alfieri·Pietro Mascagni·Nassir Navab·Nicolas Padoy

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Abstract

Developing label-efficient models is a central challenge in surgical AI due to the high cost and scarcity of expert annotation. While self-supervised foundation models adapt well to new tasks with minimal data, how label efficiency varies across different surgical tasks remains largely unexplored. Here, we introduce SURGE, a surgical foundation model trained on SurgSpectrum-30M+, the largest pretraining dataset comprising over 30 million frames, with checkpoints released to enable further research. We systematically evaluate label efficiency across 5 task categories and 15 benchmarks. These range from temporal and spatial scene understanding to fine-grained reasoning tied to instrument-anatomy interactions and safety-critical maneuvers. SURGE outperforms prior state-of-the-art on all benchmarks, even surpassing task-specific models on complex reasoning tasks. Crucially, we reveal a task-dependent scaling behavior: while scene understanding tasks saturate with minimal supervision, fine-grained reasoning tasks continue improving with substantially larger annotation budgets, providing a blueprint for allocating expert effort in complex domains. Code: https://github.com/CAMMA-public/SURGE

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Florian Philipp Stilz, Lorenzo Arboit, Vinkle Srivastav, CAMMA International Surgical Partners, Jacques Marescaux, Sergio Alfieri, Pietro Mascagni, Nassir Navab, Nicolas Padoy. 2026-09-25. A Surgical Foundation Model Reveals Task-Dependent Label Efficiency. https://arxiv.org/abs/2609.31821

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