Weakly Supervised Temporal Convolutional Networks for Fine-Grained Surgical Activity Recognition-Reference-Cited by-同舟云学术

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

Published:2023-09 Issue:9 Volume:42 Page:2592-2602
ISSN:0278-0062
Container-title:IEEE Transactions on Medical Imaging
language:
Short-container-title:IEEE Trans. Med. Imaging

Author:

Ramesh Sanat¹^ORCID,DalľAlba Diego¹^ORCID,Gonzalez Cristians²,Yu Tong³,Mascagni Pietro³^ORCID,Mutter Didier⁴,Marescaux Jacques⁵^ORCID,Fiorini Paolo¹^ORCID,Padoy Nicolas³^ORCID

Affiliation:

1. Department of Computer Science, Altair Robotics Laboratory, University of Verona, Verona, Italy

2. IHU Strasbourg, University Hospital of Strasbourg, Strasbourg, France

3. ICube Laboratory, CNRS, IHU Strasbourg, University of Strasbourg, Strasbourg, France

4. Fondazione Policlinico Universitario Agostino Gemelli, Rome, IRCCS, Italy

5. IRCAD, Strasbourg, France

Funder

European Union’s Horizon 2020 Research and Innovation Program

Marie Sklodowska-Curie

AuTonomous intraLuminAl Surgery

French State Funds managed within the Investissements d’Avenir Program

Banque Publique d’Investissement (BPI) France Project Connected Optimized Network and Data in Operating Rooms

ANR

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Science Applications,Radiological and Ultrasound Technology,Software

Link

http://xplorestaging.ieee.org/ielx7/42/10236920/10086537.pdf?arnumber=10086537

Reference42 articles.

1. Surgical gesture classification from video and kinematic data

2. Constrained Deep Weak Supervision for Histopathology Image Segmentation

3. TeCNO: Surgical Phase Recognition with Multi-stage Temporal Convolutional Networks

4. WaveNet: A generative model for raw audio;van den oord;arXiv 1609 03499,2016

5. Learning convolutional action primitives for fine-grained action recognition