210 citations · 727 across the 17 of their papers we have counts for
19 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…
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…
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…
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…
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…
Learning from a tiny dataset of manual annotations: a teacher/student approach for surgical phase recognition
Tong Yu, Didier Mutter, Jacques Marescaux +1
Vision algorithms capable of interpreting scenes from a real-time video stream are necessary for computer-assisted surgery systems to achieve context-aware behavior. In laparoscopi…
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…
Less is More: Surgical Phase Recognition with Less Annotations through Self-Supervised Pre-training of CNN-LSTM Networks
Gaurav Yengera, Didier Mutter, Jacques Marescaux +1
Real-time algorithms for automatically recognizing surgical phases are needed to develop systems that can provide assistance to surgeons, enable better management of operating room…
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…
Single- and Multi-Task Architectures for Surgical Workflow Challenge at M2CAI 2016
Andru P. Twinanda, Didier Mutter, Jacques Marescaux +2
The surgical workflow challenge at M2CAI 2016 consists of identifying 8 surgical phases in cholecystectomy procedures. Here, we propose to use deep architectures that are based on…
Single- and Multi-Task Architectures for Tool Presence Detection Challenge at M2CAI 2016
Andru P. Twinanda, Didier Mutter, Jacques Marescaux +2
The tool presence detection challenge at M2CAI 2016 consists of identifying the presence/absence of seven surgical tools in the images of cholecystectomy videos. Here, we propose t…
Automatic View-Point Selection for Inter-Operative Endoscopic Surveillance
Anant S. Vemuri, Stephane A. Nicolau, Jacques Marescaux +2
Esophageal adenocarcinoma arises from Barrett's esophagus, which is the most serious complication of gastroesophageal reflux disease. Strategies for screening involve periodic surv…
ORBSLAM-based Endoscope Tracking and 3D Reconstruction
Nader Mahmoud, Iñigo Cirauqui, Alexandre Hostettler +4
We aim to track the endoscope location inside the surgical scene and provide 3D reconstruction, in real-time, from the sole input of the image sequence captured by the monocular en…
EndoNet: A Deep Architecture for Recognition Tasks on Laparoscopic Videos
Andru P. Twinanda, Sherif Shehata, Didier Mutter +3
Surgical workflow recognition has numerous potential medical applications, such as the automatic indexing of surgical video databases and the optimization of real-time operating ro…