arXiv Computer Vision
3d ago

From Presence Labels to Bounding Boxes: Can Multimodal Large Language Models Scale Surgical Instrument Localization?

arXiv:2610.34218v1 Announce Type: new Abstract: Spatial localization of surgical instruments through bounding-box annotations is a foundational component of surgical scene understanding, supporting d...

By Mohamed Hamdy, Muraam Abdel-Ghani, Fatmaelzahraa Ahmed, Sifna Nasar, Mariam Ahmed, Khalid Al-Jalham, Abdulaziz Al-Ali, Shidin Balakrishnan
Hugging Face Trending Papers
Jun 25

Temporally Consistent Label Interpolation for Robust Surgical Multi-Task Learning under Challenging Conditions

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.

arXiv AI
Aug 24

Action-grounded tissue affordance enables anticipatory auto-framing that lowers surgeon cognitive workload during laparoscopic surgery

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 Computer Vision
Oct 2

Semantic RGB--Depth Based Surgical Skill Assessment in Microscopic Stereo Videos

The paper introduces a semantic RGB–Depth framework for assessing microsurgical skill from microscopic stereo videos. It fuses sparse metric stereo depth with dense monocular depth to create a dense geometric scene representation, which is combined with semantically segmented RGB streams of instruments and anatomy. Using a hierarchical attention architecture, the model achieves an F1 score of 0.938 for skill-level classification, outperforming RGB-only and depth-only baselines.

By Jecia Z. Y. Mao, Sue M. Cho, Francis X. Creighton, Deepa Galaiya, Russell H. Taylor, Manish Sahu