578 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Université de StrasbourgFR36 papers
- Centre National de la Recherche ScientifiqueFR34 papers
- Agostino Gemelli University PolyclinicIT14 papers
- Institut de Recherche contre les Cancers de l’Appareil DigestifFR14 papers
- Hôpitaux Universitaires de StrasbourgFR12 papers
- InsermFR10 papers
- Laboratoire des Sciences de l'Ingénieur, de l'Informatique et de l'ImagerieFR8 papers
- German Cancer Research CenterDE6 papers
- Heidelberg UniversityDE6 papers
- National Center for Tumor DiseasesDE6 papers
- University College LondonGB6 papers
- Istituti di Ricovero e Cura a Carattere ScientificoIT5 papers
42 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…
A digital twin for microwave liver treatment replanning
Ilias Nahmed, Francesco Dettori, Juan Verde +3
Purpose: MicroWave Ablation (MWA) modeling and simulation bear great potential for loco-regional treatment of liver tumors. However, accurately positioning the antenna according to…
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…
Federated Learning for Surgical Vision in Appendicitis Classification: Results of the FedSurg EndoVis 2024 Challenge
Max Kirchner, Hanna Hoffmann, Alexander C. Jenke +16
Developing generalizable surgical AI requires multi-institutional data, yet privacy constraints preclude direct data sharing, making Federated Learning (FL) a natural candidate. It…
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…
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…
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…
Towards Real-time Intrahepatic Vessel Identification in Intraoperative Ultrasound-Guided Liver Surgery
Karl-Philippe Beaudet, Alexandros Karargyris, Sidaty El Hadramy +4
While laparoscopic liver resection is less prone to complications and maintains patient outcomes compared to traditional open surgery, its complexity hinders widespread adoption du…
Self-supervised Learning via Cluster Distance Prediction for Operating Room Context Awareness
Idris Hamoud, Alexandros Karargyris, Aidean Sharghi +2
Semantic segmentation and activity classification are key components to creating intelligent surgical systems able to understand and assist clinical workflow. In the Operating Room…
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…
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…
Understanding metric-related pitfalls in image analysis validation
Annika Reinke, Minu D. Tizabi, Michael Baumgartner +75
Validation metrics are key for the reliable tracking of scientific progress and for bridging the current chasm between artificial intelligence (AI) research and its translation int…
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…
Rendezvous in Time: An Attention-based Temporal Fusion approach for Surgical Triplet Recognition
Saurav Sharma, Chinedu Innocent Nwoye, Didier Mutter +1
One of the recent advances in surgical AI is the recognition of surgical activities as triplets of (instrument, verb, target). Albeit providing detailed information for computer-as…
Metrics reloaded: Recommendations for image analysis validation
Lena Maier-Hein, Annika Reinke, Patrick Godau +71
Increasing evidence shows that flaws in machine learning (ML) algorithm validation are an underestimated global problem. Particularly in automatic biomedical image analysis, chosen…
Data Splits and Metrics for Method Benchmarking on Surgical Action Triplet Datasets
Chinedu Innocent Nwoye, Nicolas Padoy
In addition to generating data and annotations, devising sensible data splitting strategies and evaluation metrics is essential for the creation of a benchmark dataset. This practi…
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…
Federated Cycling (FedCy): Semi-supervised Federated Learning of Surgical Phases
Hasan Kassem, Deepak Alapatt, Pietro Mascagni +3
Recent advancements in deep learning methods bring computer-assistance a step closer to fulfilling promises of safer surgical procedures. However, the generalizability of such meth…
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…