Search Results for author: Didier Mutter

Found 13 papers, 5 papers with code

Rendezvous: Attention Mechanisms for the Recognition of Surgical Action Triplets in Endoscopic Videos

2 code implementations7 Sep 2021 Chinedu Innocent Nwoye, Tong Yu, Cristians Gonzalez, Barbara Seeliger, Pietro Mascagni, Didier Mutter, Jacques Marescaux, Nicolas Padoy

To achieve this task, we introduce our new model, the Rendezvous (RDV), which recognizes triplets directly from surgical videos by leveraging attention at two different levels.

Action Triplet Recognition

Multi-Task Temporal Convolutional Networks for Joint Recognition of Surgical Phases and Steps in Gastric Bypass Procedures

no code implementations24 Feb 2021 Sanat Ramesh, Diego Dall'Alba, Cristians Gonzalez, Tong Yu, Pietro Mascagni, Didier Mutter, Jacques Marescaux, Paolo Fiorini, Nicolas Padoy

Conclusion: In this work, we present a multi-task multi-stage temporal convolutional network for surgical activity recognition, which shows improved results compared to single-task models on the Bypass40 gastric bypass dataset with multi-level annotations.

Activity Recognition

Weakly Supervised Convolutional LSTM Approach for Tool Tracking in Laparoscopic Videos

1 code implementation4 Dec 2018 Chinedu Innocent Nwoye, Didier Mutter, Jacques Marescaux, Nicolas Padoy

Results: We build a baseline tracker on top of the CNN model and demonstrate that our approach based on the ConvLSTM outperforms the baseline in tool presence detection, spatial localization, and motion tracking by over 5. 0%, 13. 9%, and 12. 6%, respectively.

Instrument Recognition Surgical tool detection +2

Learning from a tiny dataset of manual annotations: a teacher/student approach for surgical phase recognition

no code implementations30 Nov 2018 Tong Yu, Didier Mutter, Jacques Marescaux, Nicolas Padoy

Vision algorithms capable of interpreting scenes from a real-time video stream are necessary for computer-assisted surgery systems to achieve context-aware behavior.

Future-State Predicting LSTM for Early Surgery Type Recognition

no code implementations28 Nov 2018 Siddharth Kannan, Gaurav Yengera, Didier Mutter, Jacques Marescaux, Nicolas Padoy

This work presents a novel approach for the early recognition of the type of a laparoscopic surgery from its video.

Weakly-Supervised Learning for Tool Localization in Laparoscopic Videos

1 code implementation14 Jun 2018 Armine Vardazaryan, Didier Mutter, Jacques Marescaux, Nicolas Padoy

We propose a deep architecture, trained solely on image level annotations, that can be used for both tool presence detection and localization in surgical videos.

Surgical tool detection

Less is More: Surgical Phase Recognition with Less Annotations through Self-Supervised Pre-training of CNN-LSTM Networks

no code implementations22 May 2018 Gaurav Yengera, Didier Mutter, Jacques Marescaux, Nicolas Padoy

In this work, we propose a new self-supervised pre-training approach based on the prediction of remaining surgery duration (RSD) from laparoscopic videos.

RSDNet: Learning to Predict Remaining Surgery Duration from Laparoscopic Videos Without Manual Annotations

no code implementations9 Feb 2018 Andru Putra Twinanda, Gaurav Yengera, Didier Mutter, Jacques Marescaux, Nicolas Padoy

In this paper, we propose a deep learning pipeline, referred to as RSDNet, which automatically estimates the remaining surgery duration (RSD) intraoperatively by using only visual information from laparoscopic videos.

Single- and Multi-Task Architectures for Tool Presence Detection Challenge at M2CAI 2016

no code implementations27 Oct 2016 Andru P. Twinanda, Didier Mutter, Jacques Marescaux, Michel de Mathelin, Nicolas Padoy

The tool presence detection challenge at M2CAI 2016 consists of identifying the presence/absence of seven surgical tools in the images of cholecystectomy videos.

Single- and Multi-Task Architectures for Surgical Workflow Challenge at M2CAI 2016

no code implementations27 Oct 2016 Andru P. Twinanda, Didier Mutter, Jacques Marescaux, Michel de Mathelin, Nicolas Padoy

On top of these architectures we propose to use two different approaches to enforce the temporal constraints of the surgical workflow: (1) HMM-based and (2) LSTM-based pipelines.

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