Liang Qiu

Orcid: 0000-0002-6784-1627

Affiliations:
  • National University of Singapore, NUS, Department of Biomedical Engineering, Queenstown, Singapore (PhD 2021)
  • Stanford University, Department of Radiation Oncology, CA, USA
  • Agency for Science, Technology and Research, A*STAR, Institute for Infocomm Research, Singapore


According to our database1, Liang Qiu authored at least 10 papers between 2018 and 2023.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

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PhD thesis 
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Links

Online presence:

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Bibliography

2023
Federated Semi-Supervised Learning for Medical Image Segmentation via Pseudo-Label Denoising.
IEEE J. Biomed. Health Informatics, October, 2023

SAVAnet: Surgical Action-Driven Visual Attention Network for Autonomous Endoscope Control.
IEEE Trans Autom. Sci. Eng., October, 2023

2022
RSegNet: A Joint Learning Framework for Deformable Registration and Segmentation.
IEEE Trans Autom. Sci. Eng., 2022

GESRsim: Gastrointestinal Endoscopic Surgical Robot Simulator.
Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, 2022

2021
U-RSNet: An unsupervised probabilistic model for joint registration and segmentation.
Neurocomputing, 2021

Remote-Center-of-Motion Recommendation toward Brain Needle Intervention Using Deep Reinforcement Learning.
Proceedings of the IEEE International Conference on Robotics and Automation, 2021

ScoopNet: 6DOF Pose Estimation pipeline for Origami-inspired Worm Robots.
Proceedings of the IEEE International Conference on Development and Learning, 2021

2020
Endoscope navigation with SLAM-based registration to computed tomography for transoral surgery.
Int. J. Intell. Robotics Appl., 2020

2018
Preoperative-Image Guided Neurosurgical Navigation Procedures with Electromagnetic Tracking: An Effective Pipeline and A Cadaver Study.
Proceedings of the 3rd International Conference on Advanced Robotics and Mechatronics, 2018

Endoscope Navigation and 3D Reconstruction of Oral Cavity by Visual SLAM With Mitigated Data Scarcity.
Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2018


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