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:2608.02471v2 Announce Type: replace-cross
Abstract: In laparoscopy, surgeon gaze tracks where the instruments will act; easing this demand through visual attention modeling requires dense label...
By Jiayu Gu, Yiwei Wang, Jie Zhang, Guojun Cao, Keshen Lyu, Song Zhou, Yimeng Chen, Haorui Wang, Qingmin Feng, Shenchao Shi, Hongkuan Shi, Qiuyu Yu, Qiang Xie, Huan Zhao, Wenbin Chen, Caihua Xiong, Chidan Wan, Jing Samantha Pan, Xiong Cai, Han Ding
arXiv:2608. 11204v1 Announce Type: cross Abstract: Learning reliable surgical manipulation policies is bottlenecked by the scarcity of action-labeled demonstrations: teleoperated surgical robot (e.
By Wenrui Bao, Tianyun Jiang, Zhiben Chen, Ser-Nam Lim, Peter D. Peng, Yuzhang Shang
arXiv:2606. 29247v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models represent a promising direction for embodied intelligence in surgical robotics.
By Jiashuo Sun, Yue He, Wenxuan Liu, Tao Mao, Jiazheng Wang, Xiang Chen, Min Liu
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
Effective multi-task learning for surgical scene understanding is fundamentally hindered by annotation granularity mismatch; temporal workflow tasks such as phase recognition, step recognition and anticipation benefit from dense frame-level supervision, whereas pixel-level spatial tasks including instrument segmentation and action recognition are only sparsely annotated on selected keyframes due to prohibitive labeling costs. This supervision imbalance undermines shared representation learning and limits joint optimization across heterogeneous surgical tasks.