SurgAtlas is the largest surgical video‑language dataset, containing 15,291 videos (2,391 hours) across 18 specialties and over 5,000 procedure types, all sourced from public YouTube. It uniquely includes open‑surgery videos at scale (6,182) alongside more than 9,000 minimally invasive recordings, and introduces standardized benchmarks for open‑surgery video understanding. The dataset offers a rich, multi‑tier annotation schema—segment‑level captions, step/phase descriptions, video‑level surgical narratives, and reasoning‑oriented VQA pairs—validated by experts and built through an automated LLM‑enriched pipeline.
"whyItMatters":"SurgAtlas provides an unprecedentedly large, diverse, and clinically validated resource that can train and benchmark multimodal surgical AI models, advancing the development of next‑generation foundation models for surgery."
By Filippos Bellos, Andre S. Gala-Garza, Miaowei Wang, Alyssa M. Hardin, Ahmad M. Hider, Li Yayuan, Jing Bi, Susan Liang, Chenliang Xu, Donald S. Likosky, Jason J. Corso
arXiv:2603.29962v4 Announce Type: replace
Abstract: Surgical procedures are inherently complex and risky, requiring extensive expertise and constant focus to navigate evolving intraoperative scenes....
By Shi Li, Vinkle Srivastav, Nicolas Chanel, Saurav Sharma, Nabani Banik, Lorenzo Arboit, Kun Yuan, Pietro Mascagni, Nicolas Padoy
SurgRAW introduces a multi‑agent, chain‑of‑thought workflow for robotic surgical video analysis, leveraging a new SurgCoTBench benchmark with 14,256 QA pairs across five surgical tasks. The system uses an orchestrator to split scene understanding into two reasoning streams, panel‑discussion‑style collaboration among task‑specific agents, and retrieval‑augmented generation to incorporate surgical knowledge. Experiments show SurgRAW outperforms mainstream vision‑language models and a supervised baseline by 14.61% accuracy.
By Chang Han Low, Ziyue Wang, Tianyi Zhang, Zhu Zhuo, Zhitao Zeng, Evangelos B. Mazomenos, Yueming Jin
arXiv:2609.21402v1 Announce Type: new
Abstract: Surgical instrument segmentation (SIS) plays a critical role in robotic assistance and surgical workflow analysis. However, most existing SIS methods f...
By Zhibo Zhang, Qijie Wang, Zengqiang Yan
arXiv:2608.30872v1 Announce Type: new
Abstract: Objective assessment of surgical technical skill is important for surgical training and structured feedback, but current workflows remain dependent on...
By Chaohui Dang, Zheheng Jiang, James Glasbey, David Luke, Theodoros Arvanitis, Le Zhang
SurgMotion is a video-native foundation model that replaces pixel-level reconstruction with latent motion prediction for surgical video analysis. It introduces motion-guided masked prediction, spatiotemporal affinity self-distillation, and spatiotemporal feature diversity regularization to focus on semantically meaningful regions and avoid representation collapse. Trained on SurgMotion-15M, the largest surgical video dataset, it outperforms state-of-the-art methods across 17 benchmarks, improving workflow recognition, action triplet recognition, skill assessment, polyp segmentation, and depth estimation.
By Jinlin Wu, Felix Holm, Chuxi Chen, An Wang, Yaxin Hu, Xiaofan Ye, Zelin Zang, Miao Xu, Lihua Zhou, Huai Liao, Danny T. M. Chan, Ming Feng, Wai S. Poon, Hongliang Ren, Dong Yi, Nassir Navab, Gaofeng Meng, Jiebo Luo, Hongbin Liu, Zhen Lei