Multi-Class Detection of Laparoscopic Instruments for the Intelligent Box-Trainer System Using Faster R-CNN Architecture-Reference-Cited by-同舟云学术

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Multi-Class Detection of Laparoscopic Instruments for the Intelligent Box-Trainer System Using Faster R-CNN Architecture

Published:2021-01-21 Issue: Volume: Page:
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Container-title:2021 IEEE 19th World Symposium on Applied Machine Intelligence and Informatics (SAMI)
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Author:

Fathabadi Fatemeh Rashidi,Grantner Janos L.,Shebrain Saad A,Abdel-Qader Ikhlas

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9378591/9378608/09378617.pdf?arnumber=9378617

Cited by 15 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Depth over RGB: automatic evaluation of open surgery skills using depth camera;International Journal of Computer Assisted Radiology and Surgery;2024-05-15

2. Face, Content Validity and Correlation Study of a Laparoscopic Trainer with Port Configuration Based on Quantitative aspects of Performance; Pilot Study;2023 Global Medical Engineering Physics Exchanges/Pacific Health Care Engineering (GMEPE/PAHCE);2023-03-27

3. Autonomous sequential surgical skills assessment for the peg transfer task in a laparoscopic box-trainer system with three cameras;Robotica;2023-03-03

4. 3D Autonomous Surgeon’s Hand Movement Assessment Using a Cascaded Fuzzy Supervisor in Multi-Thread Video Processing;Sensors;2023-02-27

5. Robotic-Assisted Laparoscopic Adjustment: A Meta-Analysis and Review;IEEE Transactions on Instrumentation and Measurement;2023

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