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SATR-DL: Improving Surgical Skill Assessment and Task Recognition in
  Robot-assisted Surgery with Deep Neural Networks

SATR-DL: Improving Surgical Skill Assessment and Task Recognition in Robot-assisted Surgery with Deep Neural Networks

15 June 2018
Ziheng Wang
A. M. Fey
ArXiv (abs)PDFHTML

Papers citing "SATR-DL: Improving Surgical Skill Assessment and Task Recognition in Robot-assisted Surgery with Deep Neural Networks"

5 / 5 papers shown
SURGIVID: Annotation-Efficient Surgical Video Object Discovery
SURGIVID: Annotation-Efficient Surgical Video Object Discovery
Çağhan Köksal
Ghazal Ghazaei
Nassir Navab
157
2
0
12 Sep 2024
Uncertainty-aware Self-supervised Learning for Cross-domain Technical
  Skill Assessment in Robot-assisted Surgery
Uncertainty-aware Self-supervised Learning for Cross-domain Technical Skill Assessment in Robot-assisted SurgeryIEEE Transactions on Medical Robotics and Bionics (TMRB), 2023
Ziheng Wang
A. Mariani
A. Menciassi
Elena De Momi
A. M. Fey
228
4
0
28 Apr 2023
Predicting the Timing of Camera Movements From the Kinematics of
  Instruments in Robotic-Assisted Surgery Using Artificial Neural Networks
Predicting the Timing of Camera Movements From the Kinematics of Instruments in Robotic-Assisted Surgery Using Artificial Neural NetworksIEEE Transactions on Medical Robotics and Bionics (IEEE T-MRB), 2021
Hanna Kossowsky
I. Nisky
279
7
0
23 Sep 2021
Accelerating Surgical Robotics Research: A Review of 10 Years With the
  da Vinci Research Kit
Accelerating Surgical Robotics Research: A Review of 10 Years With the da Vinci Research KitIEEE robotics & automation magazine (IEEE Robot. Autom. Mag.), 2021
C. D’Ettorre
A. Mariani
A. Stilli
Ferdinando Rodriguez y Baena
Pietro Valdastri
...
Russell H. Taylor
Gregory S. Fischer
S. DiMaio
A. Menciassi
Danail Stoyanov
328
103
0
20 Apr 2021
Deep Neural Networks for the Assessment of Surgical Skills: A Systematic
  Review
Deep Neural Networks for the Assessment of Surgical Skills: A Systematic ReviewThe Journal of Defence Modeling and Simulation: Applications, Methodology, Technology (JDMS), 2021
Erim Yanik
Xavier Intes
Uwe Krüger
Pingkun Yan
D. Diller
Brian Van Voorst
Basiel Makled
Jack Norfleet
S. De
230
48
0
03 Mar 2021
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