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Zhenghan Fang
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2020 – today
- 2024
- [j9]Jian Chen
, Shilin Ye, Zhuwu Jiang, Zhenghan Fang:
Image Deblurring Using Feedback Mechanism and Dual Gated Attention Network. Neural Process. Lett. 56(2): 88 (2024) - [c8]Zhenghan Fang, Sam Buchanan, Jeremias Sulam:
What's in a Prior? Learned Proximal Networks for Inverse Problems. ICLR 2024 - [c7]Tiewen Chen, Shanmin Yang, Shu Hu, Zhenghan Fang, Ying Fu, Xi Wu, Xin Wang:
Masked Conditional Diffusion Model for Enhancing Deepfake Detection. IJCNN 2024: 1-7 - [c6]Zhenghan Fang, Lirida Naviner, Wen Wang, Wei Le, Hao Cai:
Towards Reliability MRAM for Energy-Efficient Spin-orbit Torque Switching. SBCCI 2024: 1-5 - [i3]Tiewen Chen, Shanmin Yang, Shu Hu, Zhenghan Fang, Ying Fu, Xi Wu, Xin Wang:
Masked Conditional Diffusion Model for Enhancing Deepfake Detection. CoRR abs/2402.00541 (2024) - 2023
- [j8]Jian Chen
LMSA-Net: A lightweight multi-scale aware network for retinal vessel segmentation. Int. J. Imaging Syst. Technol. 33(5): 1515-1530 (2023) - [j7]Zhenghan Fang
DeepSTI: Towards tensor reconstruction using fewer orientations in susceptibility tensor imaging. Medical Image Anal. 87: 102829 (2023) - [j6]Zhenghan Fang
Annotation-Efficient COVID-19 Pneumonia Lesion Segmentation Using Error-Aware Unified Semisupervised and Active Learning. IEEE Trans. Artif. Intell. 4(2): 255-267 (2023) - [c5]Zhenghan Fang, Hyeong-Geol Shin
WaveSep: A Flexible Wavelet-Based Approach for Source Separation in Susceptibility Imaging. MLCN@MICCAI 2023: 56-66 - [i2]Zhenghan Fang, Sam Buchanan, Jeremias Sulam:
What's in a Prior? Learned Proximal Networks for Inverse Problems. CoRR abs/2310.14344 (2023) - 2022
- [c4]Yuning Gu, Yongsheng Pan
Deep-Learning Based T1 and T2 Quantification from Undersampled Magnetic Resonance Fingerprinting Data to Track Tracer Kinetics in Small Laboratory Animals. MICCAI (6) 2022: 432-441 - [i1]Zhenghan Fang, Kuo-Wei Lai, Peter C. M. van Zijl, Xu Li, Jeremias Sulam:
DeepSTI: Towards Tensor Reconstruction using Fewer Orientations in Susceptibility Tensor Imaging. CoRR abs/2209.04504 (2022) - 2021
- [j5]Jian Chen
Harmonized neonatal brain MR image segmentation model for cross-site datasets. Biomed. Signal Process. Control. 69: 102810 (2021) - [j4]Jian Chen
Automatic brain extraction from 3D fetal MR image with deep learning-based multi-step framework. Comput. Medical Imaging Graph. 88: 101848 (2021) - 2020
- [j3]Yong Chen, Zhenghan Fang
High-resolution 3D MR Fingerprinting using parallel imaging and deep learning. NeuroImage 206 (2020) - [j2]Zhenghan Fang
Erratum to "Deep Learning for Fast and Spatially Constrained Tissue Quantification From Highly Accelerated Data in Magnetic Resonance Fingerprinting". IEEE Trans. Medical Imaging 39(2): 543 (2020)
2010 – 2019
- 2019
- [j1]Zhenghan Fang, Yong Chen, Mingxia Liu, Lei Xiang, Qian Zhang, Qian Wang, Weili Lin, Dinggang Shen:
Deep Learning for Fast and Spatially Constrained Tissue Quantification From Highly Accelerated Data in Magnetic Resonance Fingerprinting. IEEE Trans. Medical Imaging 38(10): 2375-2388 (2019) - [c3]Zhenghan Fang, Yong Chen, Dong Nie, Weili Lin, Dinggang Shen:
RCA-U-Net: Residual Channel Attention U-Net for Fast Tissue Quantification in Magnetic Resonance Fingerprinting. MICCAI (3) 2019: 101-109 - [c2]Jian Chen
CNS: CycleGAN-Assisted Neonatal Segmentation Model for Cross-Datasets. GLMI@MICCAI 2019: 172-179 - 2018
- [c1]Zhenghan Fang, Yong Chen, Mingxia Liu
Deep Learning for Fast and Spatially-Constrained Tissue Quantification from Highly-Undersampled Data in Magnetic Resonance Fingerprinting (MRF). MLMI@MICCAI 2018: 398-405
Coauthor Index
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