210 citations · 909 across the 23 of their papers we have counts for
6 papers · 1 filter
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…