一种手术基础模型揭示任务依赖的标签效率
A Surgical Foundation Model Reveals Task-Dependent Label Efficiency
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中文总结 AI 辅助
本研究提出手术基础模型SURGE，在超3000万帧数据集上预训练，系统评估5类15个基准，发现场景理解任务低标签饱和而细粒度推理任务需更多标注，为复杂领域标注分配提供蓝图。
中文摘要 AI 辅助
开发标签高效模型是手术人工智能领域的一个核心挑战，因为专家标注成本高昂且稀缺。虽然自监督基础模型能够以最少的数据很好地适应新任务，但标签效率在不同手术任务之间的差异在很大程度上仍未得到探索。在此，我们介绍了SURGE，一种手术基础模型，它是在SurgSpectrum-30M+上训练的，这是最大的预训练数据集，包含超过3000万帧，我们发布了检查点以促进进一步研究。我们系统地评估了5个任务类别和15个基准上的标签效率。这些任务涵盖从时间和空间场景理解到与器械-解剖结构交互及安全关键操作相关的细粒度推理。SURGE在所有基准上都优于先前的最先进技术，甚至在复杂推理任务上超越了任务专用模型。至关重要的是，我们揭示了一种任务依赖的缩放行为：虽然场景理解任务在极少监督下即达到饱和，但细粒度推理任务在标注预算大幅增加时持续改进，这为在复杂领域分配专家工作提供了蓝图。代码：此https URL
英文摘要
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
