4 papers
Surg-R1: A Hierarchical Reasoning Foundation Model for Scalable and Interpretable Surgical Decision Support with Multi-Center Clinical Validation
Jian Jiang, Chenxi Lin, Yiming Gu +24
Surgical scene understanding demands not only accurate predictions but also interpretable reasoning that surgeons can verify against clinical expertise. However, existing surgical…
See in Depth: Training-Free Surgical Scene Segmentation with Monocular Depth Priors
Kunyi Yang, Qingyu Wang, Cheng Yuan +1
Pixel-wise segmentation of laparoscopic scenes is essential for computer-assisted surgery but difficult to scale due to the high cost of dense annotations. We propose depth-guided…
The SAGES Critical View of Safety Challenge: A Global Benchmark for AI-Assisted Surgical Quality Assessment
Deepak Alapatt, Jennifer Eckhoff, Zhiliang Lyu +38
Advances in artificial intelligence (AI) for surgical quality assessment promise to democratize access to expertise, with applications in training, guidance, and accreditation. Thi…
Systematic Evaluation and Guidelines for Segment Anything Model in Surgical Video Analysis
Cheng Yuan, Jian Jiang, Kunyi Yang +11
Surgical video segmentation is critical for AI to interpret spatial-temporal dynamics in surgery, yet model performance is constrained by limited annotated data. The SAM2 model, pr…