Zhenxing Huang
Orcid: 0000-0001-5012-1992
According to our database1,
Zhenxing Huang
authored at least 12 papers
between 2020 and 2024.
Collaborative distances:
Collaborative distances:
Timeline
Legend:
Book In proceedings Article PhD thesis Dataset OtherLinks
Online presence:
-
on orcid.org
On csauthors.net:
Bibliography
2024
MMCA-NET: A Multimodal Cross Attention Transformer Network for Nasopharyngeal Carcinoma Tumor Segmentation Based on a Total-Body PET/CT System.
IEEE J. Biomed. Health Informatics, September, 2024
Accurate Whole-Brain Image Enhancement for Low-Dose Integrated PET/MR Imaging Through Spatial Brain Transformation.
IEEE J. Biomed. Health Informatics, September, 2024
OIF-Net: An Optical Flow Registration-Based PET/MR Cross-Modal Interactive Fusion Network for Low-Count Brain PET Image Denoising.
IEEE Trans. Medical Imaging, April, 2024
Quantitative pharmacokinetic parameter K<sup>trans</sup> map assists in regional segmentation of nasopharyngeal carcinoma in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI).
Biomed. Signal Process. Control., January, 2024
2023
Low-dose dynamic cerebral perfusion CT reconstruction based on voxel-level TAC correction (VTC).
Biomed. Signal Process. Control., September, 2023
MLNAN: Multi-level noise-aware network for low-dose CT imaging implemented with constrained cycle Wasserstein generative adversarial networks.
Artif. Intell. Medicine, September, 2023
Adaptive weighted curvature-based active contour for ultrasonic and 3T/5T MR image segmentation.
Signal Process., 2023
2022
Comput. Methods Programs Biomed., 2022
2021
Learning a Deep CNN Denoising Approach Using Anatomical Prior Information Implemented With Attention Mechanism for Low-Dose CT Imaging on Clinical Patient Data From Multiple Anatomical Sites.
IEEE J. Biomed. Health Informatics, 2021
Considering anatomical prior information for low-dose CT image enhancement using attribute-augmented Wasserstein generative adversarial networks.
Neurocomputing, 2021
FaNet: fast assessment network for the novel coronavirus (COVID-19) pneumonia based on 3D CT imaging and clinical symptoms.
Appl. Intell., 2021
2020
J. Real Time Image Process., 2020