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…
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…
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…
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…