210 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 ScientifiqueFR18 papers
- Université de StrasbourgFR18 papers
- Institut de Chirurgie Guidée par l'ImageFR14 papers
- Institut de Recherche contre les Cancers de l’Appareil DigestifFR10 papers
- Istituti di Ricovero e Cura a Carattere ScientificoIT7 papers
- InsermFR6 papers
- Laboratoire des Sciences de l'Ingénieur, de l'Informatique et de l'ImagerieFR3 papers
- Università Cattolica del Sacro CuoreIT3 papers
- German Cancer Research CenterDE2 papers
- Heidelberg UniversityDE2 papers
- Hôpital Civil, StrasbourgFR2 papers
- Indian Institute of Technology KharagpurIN2 papers
20 papers
GEN-Guard: Correcting Generalization Failures for Deployable Federated Surgical AI
Julia Alekseenko, Pietro Mascagni, AI4SafeChole Consortium +1
Federated Learning (FL) in surgical video AI enables collaborative model training without sharing sensitive data. However, standard evaluation practices - selecting the "best" glob…
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…
TrackRAD2025 challenge dataset: Real-time tumor tracking for MRI-guided radiotherapy
Yiling Wang, Elia Lombardo, Adrian Thummerer +25
Purpose: Magnetic resonance imaging (MRI) to visualize anatomical motion is becoming increasingly important when treating cancer patients with radiotherapy. Hybrid MRI-linear accel…
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…
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…
Surgical Text-to-Image Generation
Chinedu Innocent Nwoye, Rupak Bose, Kareem Elgohary +5
Acquiring surgical data for research and development is significantly hindered by high annotation costs and practical and ethical constraints. Utilizing synthetically generated ima…
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…
Development and validation of an interpretable machine learning-based calculator for predicting 5-year weight trajectories after bariatric surgery: a multinational retrospective cohort SOPHIA study
Patrick Saux, Pierre Bauvin, Violeta Raverdy +31
Background Weight loss trajectories after bariatric surgery vary widely between individuals, and predicting weight loss before the operation remains challenging. We aimed to develo…
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…
CholecTriplet2022: Show me a tool and tell me the triplet -- an endoscopic vision challenge for surgical action triplet detection
Chinedu Innocent Nwoye, Tong Yu, Saurav Sharma +46
Formalizing surgical activities as triplets of the used instruments, actions performed, and target anatomies is becoming a gold standard approach for surgical activity modeling. Th…
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…
CholecTriplet2021: A benchmark challenge for surgical action triplet recognition
Chinedu Innocent Nwoye, Deepak Alapatt, Tong Yu +59
Context-aware decision support in the operating room can foster surgical safety and efficiency by leveraging real-time feedback from surgical workflow analysis. Most existing works…
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…
MedPerf: Open Benchmarking Platform for Medical Artificial Intelligence using Federated Evaluation
Alexandros Karargyris, Renato Umeton, Micah J. Sheller +39
Medical AI has tremendous potential to advance healthcare by supporting the evidence-based practice of medicine, personalizing patient treatment, reducing costs, and improving prov…
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…
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…
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…