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
We introduce SurgAtlas, the largest surgical video-language dataset to date, comprising 15,291 videos (2,391 hours) spanning 18 surgical specialties and over 5,000 procedure types, sourced entirely from publicly available YouTube content. SurgAtlas is also the first surgical video-language dataset to include open surgery at scale, with 6,182 open procedure videos alongside over 9,000 minimally invasive recordings, and the first to establish standardized benchmarks for open-surgery video understanding.
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. 14015v1 Announce Type: cross Abstract: Understanding tens-of-minutes surgical videos requires long-horizon temporal reasoning, answering what happens before, after, or across stages of a procedure by grounding the question in visual evidence spread across time.
By Yingying Fan, Penghui Du, Leyan Zhu, Runze He, Zimeng Wu, Yuxuan Zhang, Liang Chen, Jiahao Xie, Jiangtang Wang, Shuai Shao, Anchao Yang, Yutong Bai, Yan Wang
Recent advances in Vision-Language Models (VLMs) have led to rapid progress in video understanding across a wide range of benchmark tasks. However, existing evaluations largely focus on short-term rea...
arXiv:2610.10156v1 Announce Type: new
Abstract: Recent advances in Vision-Language Models (VLMs) have led to rapid progress in video understanding across a wide range of benchmark tasks. However, exi...
By Leon Mayer, Lucas Luttner, Patrick Godau, Kai Fritzsche, Annika Reinke, Leonie Boland, Jule Brandt, Janne Heinecke, Chloe K. Nobuhara, Niklas Holzwarth, Evangelia Christodoulou, Marcel Knopp, Dominik Michael, Pascale Piermarco, Saliq Neyaz, Korhan Derin \"Ozarslan, Jakob Hennighausen, Carlos Aumente-Maestro, Tim R\"adsch, Dheeraj Baji, Peter Maximilian Full, Finn Aichholz, Justus Veit Erpenbeck, Linus Finn Schott, Bastian Winkelhausen, Claas de Boer, Bianca G\"uttner, Anneli Hummel, Gregor Just, Max Kirchner, Chenyang Li, Rozenn Raffaut, Ariel Rodriguez, Danush Kumar Venkatesh, Kevin Wang, Jinjing Xu, Mona Sheikh Zeinoddin, Salman Khan, Thomas M. Pausch, Stefanie Speidel, Danail Stoyanov, Daniel A. Hashimoto, Fiona R. Kolbinger, Thomas G. Weiser, Lena Maier-Hein
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
arXiv:2609.37283v1 Announce Type: new
Abstract: Medical multimodal large language models (MLLMs) are increasingly expected not only to answer clinical questions, but also to localize the visual evide...
By Xuyang Cao, Enyou Liu, Jun Zhao, Zhuoyun Liu, Jintao Fei, Leo
arXiv:2601. 07761v2 Announce Type: replace Abstract: Large Vision-Language Models (LVLMs) face a fundamental dilemma in video reasoning: they are caught between the prohibitive computational costs of verbose reasoning and the hallucination risks of efficient, ungrounded approaches.
By Yanxiang Huang, Guohua Gao, Zhaoyang Wei
The paper introduces CORE‑Seg, an end‑to‑end framework that combines reasoning and segmentation for complex lesion detection in medical images. It leverages a Semantic‑Guided Prompt Adapter and a progressive training strategy from SFT to GRPO, using an adaptive dual‑granularity reward mechanism to address reward sparsity. CORE‑Seg achieves state‑of‑the‑art performance, with a mean Dice score of 37.06%—14.89% higher than the next best baseline—and reduces the failure rate to 18.42%.
By Yuxin Xie, Yuming Chen, Yishan Yang, Yi Zhou, Tao Zhou, Zhen Zhao, Jiacheng Liu, Huazhu Fu
arXiv:2608.20805v1 Announce Type: new
Abstract: Long-form video understanding remains challenging for video agents due to the mismatch between query demands and evidence acquisition strategies. Altho...
By Tianyue Wang, Xuying Wu, Yuxiang Ma, Ruiming Liang, Jiaxuan Kang, Yanchao Hao, Zheng Wei, Leigang Qu, Haiyun Guo, Jinqiao Wang