1.2k citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Centre National de la Recherche ScientifiqueFR19 papers
- Université de StrasbourgFR18 papers
- Institut de Chirurgie Guidée par l'ImageFR14 papers
- Agostino Gemelli University PolyclinicIT10 papers
- Laboratoire des Sciences de l'Ingénieur, de l'Informatique et de l'ImagerieFR8 papers
- Hôpitaux Universitaires de StrasbourgFR6 papers
- University of VeronaIT3 papers
- InsermFR2 papers
- Istituti di Ricovero e Cura a Carattere ScientificoIT2 papers
- Technical University of MunichDE2 papers
- University of PennsylvaniaUS2 papers
- Atmospheric and Space Technology Research Associates (United States)US1 paper
26 papers
Surgeons Awareness, Expectations, and Involvement with Artificial Intelligence: a Survey Pre and Post the GPT Era
Lorenzo Arboit, Dennis N. Schneider, Toby Collins +7
Artificial Intelligence (AI) is transforming medicine, with generative AI models like ChatGPT reshaping perceptions of its potential. This study examines surgeons' awareness, expec…
SurgiTrack: Fine-Grained Multi-Class Multi-Tool Tracking in Surgical Videos
Chinedu Innocent Nwoye, Nicolas Padoy
Accurate tool tracking is essential for the success of computer-assisted intervention. Previous efforts often modeled tool trajectories rigidly, overlooking the dynamic nature of s…
On-the-Fly Point Annotation for Fast Medical Video Labeling
Meyer Adrien, Mazellier Jean-Paul, Jeremy Dana +1
Purpose: In medical research, deep learning models rely on high-quality annotated data, a process often laborious and timeconsuming. This is particularly true for detection tasks w…
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…
Automated Driving Without Ethics: Meaning, Design and Real-World Implementation
Katherine Evans, Nelson de Moura, Raja Chatila +1
The ethics of automated vehicles (AV) has received a great amount of attention in recent years, specifically in regard to their decisional policies in accident situations in which…
Learning Multi-modal Representations by Watching Hundreds of Surgical Video Lectures
Kun Yuan, Vinkle Srivastav, Tong Yu +5
Recent advancements in surgical computer vision applications have been driven by vision-only models, which do not explicitly integrate the rich semantics of language into their des…
Weakly Supervised Temporal Convolutional Networks for Fine-grained Surgical Activity Recognition
Sanat Ramesh, Diego Dall'Alba, Cristians Gonzalez +6
Automatic recognition of fine-grained surgical activities, called steps, is a challenging but crucial task for intelligent intra-operative computer assistance. The development of c…
Preserving Privacy in Surgical Video Analysis Using Artificial Intelligence: A Deep Learning Classifier to Identify Out-of-Body Scenes in Endoscopic Videos
Joël L. Lavanchy, Armine Vardazaryan, Pietro Mascagni +3
Objective: To develop and validate a deep learning model for the identification of out-of-body images in endoscopic videos. Background: Surgical video analysis facilitates educatio…
Latent Graph Representations for Critical View of Safety Assessment
Aditya Murali, Deepak Alapatt, Pietro Mascagni +5
Assessing the critical view of safety in laparoscopic cholecystectomy requires accurate identification and localization of key anatomical structures, reasoning about their geometri…
Memory transformers for full context and high-resolution 3D Medical Segmentation
Loic Themyr, Clément Rambour, Nicolas Thome +2
Transformer models achieve state-of-the-art results for image segmentation. However, achieving long-range attention, necessary to capture global context, with high-resolution 3D im…
Live Laparoscopic Video Retrieval with Compressed Uncertainty
Tong Yu, Pietro Mascagni, Juan Verde +3
Searching through large volumes of medical data to retrieve relevant information is a challenging yet crucial task for clinical care. However the primitive and most common approach…
Temporally Constrained Neural Networks (TCNN): A framework for semi-supervised video semantic segmentation
Deepak Alapatt, Pietro Mascagni, Armine Vardazaryan +7
A major obstacle to building models for effective semantic segmentation, and particularly video semantic segmentation, is a lack of large and well annotated datasets. This bottlene…
Rendezvous: Attention Mechanisms for the Recognition of Surgical Action Triplets in Endoscopic Videos
Chinedu Innocent Nwoye, Tong Yu, Cristians Gonzalez +5
Out of all existing frameworks for surgical workflow analysis in endoscopic videos, action triplet recognition stands out as the only one aiming to provide truly fine-grained and c…
Intraoperative time out to promote the implementation of the critical view of safety in laparoscopic cholecystectomy: a video-based assessment of 343 procedures
Pietro Mascagni, Maria Rita Rodriguez-Luna, Takeshi Urade +7
Background: The critical view of safety (CVS) is poorly adopted in surgical practices although it is ubiquitously recommended to prevent major bile duct injuries during laparoscopi…
Robotic needle steering in deformable tissues with extreme learning machines
Pedro Henrique Suruagy Perrusi, Anna Cazzaniga, Paul Baksic +3
Control strategies for robotic needle steering in soft tissues must account for complex interactions between the needle and the tissue to achieve accurate needle tip positioning. R…
Multi-Task Temporal Convolutional Networks for Joint Recognition of Surgical Phases and Steps in Gastric Bypass Procedures
Sanat Ramesh, Diego Dall'Alba, Cristians Gonzalez +6
Purpose: Automatic segmentation and classification of surgical activity is crucial for providing advanced support in computer-assisted interventions and autonomous functionalities…
Wearable Sensors for Spatio-Temporal Grip Force Profiling
Rongrong Liu, Florent Nageotte, Philippe Zanne +2
Wearable biosensor technology enables real-time, convenient, and continuous monitoring of users behavioral signals. Such include signals relative to body motion, body temperature,…
Recognition of Instrument-Tissue Interactions in Endoscopic Videos via Action Triplets
Chinedu Innocent Nwoye, Cristians Gonzalez, Tong Yu +4
Recognition of surgical activity is an essential component to develop context-aware decision support for the operating room. In this work, we tackle the recognition of fine-grained…
The Liver Tumor Segmentation Benchmark (LiTS)
Patrick Bilic, Patrick Christ, Hongwei Bran Li +106
In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedi…
Weakly Supervised Convolutional LSTM Approach for Tool Tracking in Laparoscopic Videos
Chinedu Innocent Nwoye, Didier Mutter, Jacques Marescaux +1
Purpose: Real-time surgical tool tracking is a core component of the future intelligent operating room (OR), because it is highly instrumental to analyze and understand the surgica…
Future-State Predicting LSTM for Early Surgery Type Recognition
Siddharth Kannan, Gaurav Yengera, Didier Mutter +2
This work presents a novel approach for the early recognition of the type of a laparoscopic surgery from its video. Early recognition algorithms can be beneficial to the developmen…
Weakly-Supervised Learning for Tool Localization in Laparoscopic Videos
Armine Vardazaryan, Didier Mutter, Jacques Marescaux +1
Surgical tool localization is an essential task for the automatic analysis of endoscopic videos. In the literature, existing methods for tool localization, tracking and segmentatio…
RSDNet: Learning to Predict Remaining Surgery Duration from Laparoscopic Videos Without Manual Annotations
Andru Putra Twinanda, Gaurav Yengera, Didier Mutter +2
Accurate surgery duration estimation is necessary for optimal OR planning, which plays an important role in patient comfort and safety as well as resource optimization. It is, howe…