Zhenghan Fang

Orcid: 0000-0002-2874-6619

According to our database1, Zhenghan Fang authored at least 16 papers between 2018 and 2024.

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

Timeline

Legend:

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In proceedings 
Article 
PhD thesis 
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Links

On csauthors.net:

Bibliography

2024
Image Deblurring Using Feedback Mechanism and Dual Gated Attention Network.
Neural Process. Lett., April, 2024

Masked Conditional Diffusion Model for Enhancing Deepfake Detection.
CoRR, 2024

2023
LMSA-Net: A lightweight multi-scale aware network for retinal vessel segmentation.
Int. J. Imaging Syst. Technol., September, 2023

Annotation-Efficient COVID-19 Pneumonia Lesion Segmentation Using Error-Aware Unified Semisupervised and Active Learning.
IEEE Trans. Artif. Intell., April, 2023

DeepSTI: Towards tensor reconstruction using fewer orientations in susceptibility tensor imaging.
Medical Image Anal., 2023

What's in a Prior? Learned Proximal Networks for Inverse Problems.
CoRR, 2023

WaveSep: A Flexible Wavelet-Based Approach for Source Separation in Susceptibility Imaging.
Proceedings of the Machine Learning in Clinical Neuroimaging - 6th International Workshop, 2023

2022
Deep-Learning Based T<sub>1</sub> and T<sub>2</sub> Quantification from Undersampled Magnetic Resonance Fingerprinting Data to Track Tracer Kinetics in Small Laboratory Animals.
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2022, 2022

2021
Automatic brain extraction from 3D fetal MR image with deep learning-based multi-step framework.
Comput. Medical Imaging Graph., 2021

Harmonized neonatal brain MR image segmentation model for cross-site datasets.
Biomed. Signal Process. Control., 2021

2020
Erratum to "Deep Learning for Fast and Spatially Constrained Tissue Quantification From Highly Accelerated Data in Magnetic Resonance Fingerprinting".
IEEE Trans. Medical Imaging, 2020

High-resolution 3D MR Fingerprinting using parallel imaging and deep learning.
NeuroImage, 2020

2019
Deep Learning for Fast and Spatially Constrained Tissue Quantification From Highly Accelerated Data in Magnetic Resonance Fingerprinting.
IEEE Trans. Medical Imaging, 2019

RCA-U-Net: Residual Channel Attention U-Net for Fast Tissue Quantification in Magnetic Resonance Fingerprinting.
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2019, 2019

CNS: CycleGAN-Assisted Neonatal Segmentation Model for Cross-Datasets.
Proceedings of the Graph Learning in Medical Imaging - First International Workshop, 2019

2018
Deep Learning for Fast and Spatially-Constrained Tissue Quantification from Highly-Undersampled Data in Magnetic Resonance Fingerprinting (MRF).
Proceedings of the Machine Learning in Medical Imaging - 9th International Workshop, 2018


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