Ligong Lu
Orcid: 0000-0003-1405-0052
According to our database1,
Ligong Lu
authored at least 13 papers
between 2019 and 2024.
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Bibliography
2024
3D/2D Vessel Registration Based on Monte Carlo Tree Search and Manifold Regularization.
IEEE Trans. Medical Imaging, May, 2024
2023
Comparison Between the Stereoscopic Virtual Reality Display System and Conventional Computed Tomography Workstation in the Diagnosis and Characterization of Cerebral Arteriovenous Malformations.
J. Digit. Imaging, August, 2023
Deep Learning for Detection of Intracranial Aneurysms from Computed Tomography Angiography Images.
J. Digit. Imaging, February, 2023
2022
Prior Knowledge-Aware Fusion Network for Prediction of Macrovascular Invasion in Hepatocellular Carcinoma.
IEEE Trans. Medical Imaging, 2022
DBFU-Net: Double branch fusion U-Net with hard example weighting train strategy to segment retinal vessel.
PeerJ Comput. Sci., 2022
Two-stage hybrid network for segmentation of COVID-19 pneumonia lesions in CT images: a multicenter study.
Medical Biol. Eng. Comput., 2022
Proceedings of the Health Information Science - 11th International Conference, 2022
2021
A Deep Learning Radiomics Model to Identify Poor Outcome in COVID-19 Patients With Underlying Health Conditions: A Multicenter Study.
IEEE J. Biomed. Health Informatics, 2021
A high resolution representation network with multi-path scale for retinal vessel segmentation.
Comput. Methods Programs Biomed., 2021
Deep learning-based aggressive progression prediction from CT images of hepatocellular carcinoma.
Proceedings of the Medical Imaging 2021: Computer-Aided Diagnosis, 2021
A Multi-Stage Guidewire Tip Tracking Framework for Cardiovascular Robotic Interventions.
Proceedings of the 14th International Congress on Image and Signal Processing, 2021
Proceedings of the 14th International Congress on Image and Signal Processing, 2021
2019
Development and validation of a radiomics-based method for macrovascular invasion prediction in hepatocellular carcinoma with prognostic implication.
Proceedings of the Medical Imaging 2019: Computer-Aided Diagnosis, San Diego, 2019