24 citations · 64 across the 17 of their papers we have counts for
18 papers
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…
Where are they looking in the operating room?
Keqi Chen, Séraphin Baributsa, Lilien Schewski +5
Purpose: Gaze-following, the task of inferring where individuals are looking, has been widely studied in computer vision, advancing research in visual attention modeling, social sc…
Self-Supervised Uncalibrated Multi-View Video Anonymization in the Operating Room
Keqi Chen, Vinkle Srivastav, Armine Vardazaryan +3
Privacy preservation is a prerequisite for using video data in Operating Room (OR) research. Effective anonymization relies on the exhaustive localization of every individual; even…
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…
State-Change Learning for Prediction of Future Events in Endoscopic Videos
Saurav Sharma, Chinedu Innocent Nwoye, Didier Mutter +1
Surgical future prediction, driven by real-time AI analysis of surgical video, is critical for operating room safety and efficiency. It provides actionable insights into upcoming e…
fine-CLIP: Enhancing Zero-Shot Fine-Grained Surgical Action Recognition with Vision-Language Models
Saurav Sharma, Didier Mutter, Nicolas Padoy
While vision-language models like CLIP have advanced zero-shot surgical phase recognition, they struggle with fine-grained surgical activities, especially action triplets. This lim…
Learning from Synchronization: Self-Supervised Uncalibrated Multi-View Person Association in Challenging Scenes
Keqi Chen, Vinkle Srivastav, Didier Mutter +1
Multi-view person association is a fundamental step towards multi-view analysis of human activities. Although the person re-identification features have been proven effective, they…
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…
Multi-view Video-Pose Pretraining for Operating Room Surgical Activity Recognition
Idris Hamoud, Vinkle Srivastav, Muhammad Abdullah Jamal +3
Understanding the workflow of surgical procedures in complex operating rooms requires a deep understanding of the interactions between clinicians and their environment. Surgical ac…
When do they StOP?: A First Step Towards Automatically Identifying Team Communication in the Operating Room
Keqi Chen, Lilien Schewski, Vinkle Srivastav +5
Purpose: Surgical performance depends not only on surgeons' technical skills but also on team communication within and across the different professional groups present during the o…
Early Operative Difficulty Assessment in Laparoscopic Cholecystectomy via Snapshot-Centric Video Analysis
Saurav Sharma, Maria Vannucci, Leonardo Pestana Legori +6
Purpose: Laparoscopic cholecystectomy (LC) operative difficulty (LCOD) is highly variable and influences outcomes. Despite extensive LC studies in surgical workflow analysis, limit…
UltraSam: A Foundation Model for Ultrasound using Large Open-Access Segmentation Datasets
Adrien Meyer, Aditya Murali, Farahdiba Zarin +2
Purpose: Automated ultrasound image analysis is challenging due to anatomical complexity and limited annotated data. To tackle this, we take a data-centric approach, assembling the…
CycleSAM: Few-Shot Surgical Scene Segmentation with Cycle- and Scene-Consistent Feature Matching
Aditya Murali, Farahdiba Zarin, Adrien Meyer +3
Surgical image segmentation is highly challenging, primarily due to scarcity of annotated data. Generalist prompted segmentation models like the Segment-Anything Model (SAM) can he…
Overcoming Dimensional Collapse in Self-supervised Contrastive Learning for Medical Image Segmentation
Jamshid Hassanpour, Vinkle Srivastav, Didier Mutter +1
Self-supervised learning (SSL) approaches have achieved great success when the amount of labeled data is limited. Within SSL, models learn robust feature representations by solving…
ST(OR)2: Spatio-Temporal Object Level Reasoning for Activity Recognition in the Operating Room
Idris Hamoud, Muhammad Abdullah Jamal, Vinkle Srivastav +3
Surgical robotics holds much promise for improving patient safety and clinician experience in the Operating Room (OR). However, it also comes with new challenges, requiring strong…
Challenges in Multi-centric Generalization: Phase and Step Recognition in Roux-en-Y Gastric Bypass Surgery
Joel L. Lavanchy, Sanat Ramesh, Diego Dall'Alba +7
Most studies on surgical activity recognition utilizing Artificial intelligence (AI) have focused mainly on recognizing one type of activity from small and mono-centric surgical vi…
Encoding Surgical Videos as Latent Spatiotemporal Graphs for Object and Anatomy-Driven Reasoning
Aditya Murali, Deepak Alapatt, Pietro Mascagni +5
Recently, spatiotemporal graphs have emerged as a concise and elegant manner of representing video clips in an object-centric fashion, and have shown to be useful for downstream ta…
The Endoscapes Dataset for Surgical Scene Segmentation, Object Detection, and Critical View of Safety Assessment: Official Splits and Benchmark
Aditya Murali, Deepak Alapatt, Pietro Mascagni +8
This technical report provides a detailed overview of Endoscapes, a dataset of laparoscopic cholecystectomy (LC) videos with highly intricate annotations targeted at automated asse…