activity
20242026
collaborators

24 papers

cs.CV2026

SPIRIT: Spatio-temporal Pairwise Relational Modeling of Instrument-Tissue Interactions for Surgical Action Triplet Recognition

Saurav Sharma, Lorenzo Arboit, Nabani Banik +11

Fine-grained understanding of surgical activity is essential for context-aware assistance in the operating room, including safety monitoring, adverse event identification, and skil…

cs.CY2026

AI for Quality Assurance in the Operating Room

Pietro Mascagni, Lalith Sharan, Deepak Alapatt +1

Surgical outcomes depend not only on patient factors and postoperative care but are also strongly influenced by the quality of the operation itself. Yet, for much of mod-ern surger…

q-bio.OT2026

Current validation practice undermines surgical AI development

Annika Reinke, Ziying O. Li, Minu D. Tizabi +97

Surgical data science (SDS) is rapidly advancing, yet clinical adoption of artificial intelligence (AI) in surgery remains limited, with inadequate validation as an important contr…

cs.CV2026

S4M: 4-points to Segment Anything

Adrien Meyer, Lorenzo Arboit, Giuseppe Massimiani +3

Purpose: The Segment Anything Model (SAM) promises to ease the annotation bottleneck in medical segmentation, but overlapping anatomy and blurred boundaries make its point prompts…

cs.CV2026

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…

cs.CV2026

DExTeR: Weakly Semi-Supervised Object Detection with Class and Instance Experts for Medical Imaging

Adrien Meyer, Didier Mutter, Nicolas Padoy

Detecting anatomical landmarks in medical imaging is essential for diagnosis and intervention guidance. However, object detection models rely on costly bounding box annotations, li…