32 citations · 81 across the 34 of their papers we have counts for
37 papers
A Surgical Foundation Model Reveals Task-Dependent Label Efficiency
Florian Philipp Stilz, Lorenzo Arboit, Vinkle Srivastav +6
Developing label-efficient models is a central challenge in surgical AI due to the high cost and scarcity of expert annotation. While self-supervised foundation models adapt well t…
Self-Supervised Multi-View 3D Gaze Target Estimation via Probabilistic Ray Marching
Keqi Chen, Vinkle Srivastav, Nicolas Padoy
We present a self-supervised approach, Self-MVGTE, for estimating 3D gaze targets from multiple camera views. Unlike existing methods that independently estimate 2D gaze targets pe…
SGRNet: Spatially Guided Radiology Network for Structured Radiological Reporting of Head and Neck Cancer
Ayush Gupta, Vinkle Srivastav, Prateek Upadhya +3
Automated radiological report generation can alleviate clinical workloads and eliminate observer variability. However, standard free-text generation models pose hallucination risks…
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…
CliPPER: Contextual Video-Language Pretraining on Long-form Intraoperative Surgical Procedures for Event Recognition
Florian Stilz, Vinkle Srivastav, Nassir Navab +1
Video-language foundation models have proven to be highly effective in zero-shot applications across a wide range of tasks. A particularly challenging area is the intraoperative su…
SurgTEMP: Temporal-Aware Surgical Video Question Answering with Text-guided Visual Memory for Laparoscopic Cholecystectomy
Shi Li, Vinkle Srivastav, Shih-Min Yin +7
Surgical procedures are inherently complex and risky, requiring extensive expertise and constant focus to navigate evolving intraoperative scenes. Computer-assisted systems such as…
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…
Artificial Intelligence for the Assessment of Peritoneal Carcinosis during Diagnostic Laparoscopy for Advanced Ovarian Cancer
Riccardo Oliva, Farahdiba Zarin, Alice Zampolini Faustini +9
Advanced Ovarian Cancer (AOC) is often diagnosed at an advanced stage with peritoneal carcinosis (PC). Fagotti score (FS) assessment at diagnostic laparoscopy (DL) guides treatment…
Endoshare: A Publicly Available, Surgeons-Friendly Solution to De-Identify and Manage Surgical Videos
Lorenzo Arboit, Dennis N. Schneider, Britty Baby +3
Video-based assessment and surgical data science can advance surgical training, research, and quality improvement, yet adoption remains limited by heterogeneous recording formats a…
Current validation practice undermines surgical AI development
Annika Reinke, Ziying O. Li, Minu D. Tizabi +98
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…
End-to-End Learning of Multi-Organ Implicit Surfaces from 3D Medical Imaging Data
Farahdiba Zarin, Nicolas Padoy, Jérémy Dana +1
The fine-grained surface reconstruction of different organs from 3D medical imaging can provide advanced diagnostic support and improved surgical planning. However, the representat…
Recognizing Surgical Phases Anywhere: Few-Shot Test-time Adaptation and Task-graph Guided Refinement
Kun Yuan, Tingxuan Chen, Shi Li +9
The complexity and diversity of surgical workflows, driven by heterogeneous operating room settings, institutional protocols, and anatomical variability, present a significant chal…
Adaptation of Multi-modal Representation Models for Multi-task Surgical Computer Vision
Soham Walimbe, Britty Baby, Vinkle Srivastav +1
Surgical AI often involves multiple tasks within a single procedure, like phase recognition or assessing the Critical View of Safety in laparoscopic cholecystectomy. Traditional mo…
Multi-modal Representations for Fine-grained Multi-label Critical View of Safety Recognition
Britty Baby, Vinkle Srivastav, Pooja P. Jain +3
The Critical View of Safety (CVS) is crucial for safe laparoscopic cholecystectomy, yet assessing CVS criteria remains a complex and challenging task, even for experts. Traditional…
Learning from Sparse Point Labels for Dense Carcinosis Localization in Advanced Ovarian Cancer Assessment
Farahdiba Zarin, Riccardo Oliva, Vinkle Srivastav +7
Learning from sparse labels is a challenge commonplace in the medical domain. This is due to numerous factors, such as annotation cost, and is especially true for newly introduced…
A Skull-Adaptive Framework for AI-Based 3D Transcranial Focused Ultrasound Simulation
Vinkle Srivastav, Juliette Puel, Jonathan Vappou +3
Transcranial focused ultrasound (tFUS) is an emerging modality for non-invasive brain stimulation and therapeutic intervention, offering millimeter-scale spatial precision and the…
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…
Text-driven Adaptation of Foundation Models for Few-shot Surgical Workflow Analysis
Tingxuan Chen, Kun Yuan, Vinkle Srivastav +2
Purpose: Surgical workflow analysis is crucial for improving surgical efficiency and safety. However, previous studies rely heavily on large-scale annotated datasets, posing challe…
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…
OphCLIP: Hierarchical Retrieval-Augmented Learning for Ophthalmic Surgical Video-Language Pretraining
Ming Hu, Kun Yuan, Yaling Shen +17
Surgical practice involves complex visual interpretation, procedural skills, and advanced medical knowledge, making surgical vision-language pretraining (VLP) particularly challeng…
Procedure-Aware Surgical Video-language Pretraining with Hierarchical Knowledge Augmentation
Kun Yuan, Vinkle Srivastav, Nassir Navab +1
Surgical video-language pretraining (VLP) faces unique challenges due to the knowledge domain gap and the scarcity of multi-modal data. This study aims to bridge the gap by address…
HecVL: Hierarchical Video-Language Pretraining for Zero-shot Surgical Phase Recognition
Kun Yuan, Vinkle Srivastav, Nassir Navab +1
Natural language could play an important role in developing generalist surgical models by providing a broad source of supervision from raw texts. This flexible form of supervision…
SelfPose3d: Self-Supervised Multi-Person Multi-View 3d Pose Estimation
Vinkle Srivastav, Keqi Chen, Nicolas Padoy
We present a new self-supervised approach, SelfPose3d, for estimating 3d poses of multiple persons from multiple camera views. Unlike current state-of-the-art fully-supervised meth…