Search Results for author: Didier Mutter

Found 29 papers, 15 papers with code

Overcoming Dimensional Collapse in Self-supervised Contrastive Learning for Medical Image Segmentation

no code implementations22 Feb 2024 Jamshid Hassanpour, Vinkle Srivastav, Didier Mutter, Nicolas Padoy

In this work, we investigate the application of contrastive learning to the domain of medical image analysis.

The Endoscapes Dataset for Surgical Scene Segmentation, Object Detection, and Critical View of Safety Assessment: Official Splits and Benchmark

1 code implementation19 Dec 2023 Aditya Murali, Deepak Alapatt, Pietro Mascagni, Armine Vardazaryan, Alain Garcia, Nariaki Okamoto, Guido Costamagna, Didier Mutter, Jacques Marescaux, Bernard Dallemagne, Nicolas Padoy

This technical report provides a detailed overview of Endoscapes, a dataset of laparoscopic cholecystectomy (LC) videos with highly intricate annotations targeted at automated assessment of the Critical View of Safety (CVS).

Anatomy Instance Segmentation +4

Challenges in Multi-centric Generalization: Phase and Step Recognition in Roux-en-Y Gastric Bypass Surgery

1 code implementation18 Dec 2023 Joel L. Lavanchy, Sanat Ramesh, Diego Dall'Alba, Cristians Gonzalez, Paolo Fiorini, Beat Muller-Stich, Philipp C. Nett, Jacques Marescaux, Didier Mutter, Nicolas Padoy

The use of multi-centric training data, experiments 6) and 7), improves the generalization capabilities of the models, bringing them beyond the level of independent mono-centric training and validation (experiments 1) and 2)).

Activity Recognition

Encoding Surgical Videos as Latent Spatiotemporal Graphs for Object and Anatomy-Driven Reasoning

1 code implementation11 Dec 2023 Aditya Murali, Deepak Alapatt, Pietro Mascagni, Armine Vardazaryan, Alain Garcia, Nariaki Okamoto, Didier Mutter, Nicolas Padoy

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 tasks such as action recognition.

Action Recognition Anatomy +3

TRUSTED: The Paired 3D Transabdominal Ultrasound and CT Human Data for Kidney Segmentation and Registration Research

no code implementations19 Oct 2023 William Ndzimbong, Cyril Fourniol, Loic Themyr, Nicolas Thome, Yvonne Keeza, Beniot Sauer, Pierre-Thierry Piechaud, Arnaud Mejean, Jacques Marescaux, Daniel George, Didier Mutter, Alexandre Hostettler, Toby Collins

To validate the dataset's utility, 5 competitive Deep Learning models for automatic kidney segmentation were benchmarked, yielding average DICE scores from 83. 2% to 89. 1% for CT, and 61. 9% to 79. 4% for US images.

Image Registration Image Segmentation +2

Surgical Action Triplet Detection by Mixed Supervised Learning of Instrument-Tissue Interactions

1 code implementation18 Jul 2023 Saurav Sharma, Chinedu Innocent Nwoye, Didier Mutter, Nicolas Padoy

We analyze how the amount of instrument spatial annotations affects triplet detection and observe that accurate instrument localization does not guarantee better triplet detection due to the risk of erroneous associations with the verbs and targets.

Action Triplet Detection

Weakly Supervised Temporal Convolutional Networks for Fine-grained Surgical Activity Recognition

no code implementations21 Feb 2023 Sanat Ramesh, Diego Dall'Alba, Cristians Gonzalez, Tong Yu, Pietro Mascagni, Didier Mutter, Jacques Marescaux, Paolo Fiorini, Nicolas Padoy

In this work, we propose to use coarser and easier-to-annotate activity labels, namely phases, as weak supervision to learn step recognition with fewer step annotated videos.

Activity Recognition

Preserving Privacy in Surgical Video Analysis Using Artificial Intelligence: A Deep Learning Classifier to Identify Out-of-Body Scenes in Endoscopic Videos

no code implementations17 Jan 2023 Joël L. Lavanchy, Armine Vardazaryan, Pietro Mascagni, AI4SafeChole Consortium, Didier Mutter, Nicolas Padoy

Results: The internal dataset consisting of 356, 267 images from 48 videos and the two multicentric test datasets consisting of 54, 385 and 58, 349 images from 10 and 20 videos, respectively, were annotated.

Test

Latent Graph Representations for Critical View of Safety Assessment

1 code implementation8 Dec 2022 Aditya Murali, Deepak Alapatt, Pietro Mascagni, Armine Vardazaryan, Alain Garcia, Nariaki Okamoto, Didier Mutter, Nicolas Padoy

Assessing the critical view of safety in laparoscopic cholecystectomy requires accurate identification and localization of key anatomical structures, reasoning about their geometric relationships to one another, and determining the quality of their exposure.

Anatomy Image Reconstruction +2

Rendezvous in Time: An Attention-based Temporal Fusion approach for Surgical Triplet Recognition

1 code implementation30 Nov 2022 Saurav Sharma, Chinedu Innocent Nwoye, Didier Mutter, Nicolas Padoy

Focusing more on the verbs, our RiT explores the connectedness of current and past frames to learn temporal attention-based features for enhanced triplet recognition.

Action Triplet Recognition

Live Laparoscopic Video Retrieval with Compressed Uncertainty

no code implementations8 Mar 2022 Tong Yu, Pietro Mascagni, Juan Verde, Jacques Marescaux, Didier Mutter, Nicolas Padoy

Searching through large volumes of medical data to retrieve relevant information is a challenging yet crucial task for clinical care.

Retrieval Video Retrieval

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

8 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

1 code implementation30 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.

Online surgical phase recognition

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.

Vocal Bursts Type Prediction

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 Weakly-supervised Learning

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.

Management Surgical phase recognition

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

1 code implementation9 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.

Surgical phase recognition

Cannot find the paper you are looking for? You can Submit a new open access paper.