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Disambiguation of "Fang, Zhenghan"

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LMSA-Net: A lightweight multi-scale aware network for retinal vessel segmentation.

, , , and . Int. J. Imaging Syst. Technol., 33 (5): 1515-1530 (September 2023)

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Fang-Fang Tang

Xing Fang

Fang Chen

Weigui Fang

Xiangfan Fang

 

Other publications of authors with the same name

Deep Learning for Fast and Spatially Constrained Tissue Quantification From Highly Accelerated Data in Magnetic Resonance Fingerprinting., , , , , , , and . IEEE Trans. Medical Imaging, 38 (10): 2375-2388 (2019)Deep-Learning Based T1 and T2 Quantification from Undersampled Magnetic Resonance Fingerprinting Data to Track Tracer Kinetics in Small Laboratory Animals., , , , , , , , , and 1 other author(s). MICCAI (6), volume 13436 of Lecture Notes in Computer Science, page 432-441. Springer, (2022)Erratum to "Deep Learning for Fast and Spatially Constrained Tissue Quantification From Highly Accelerated Data in Magnetic Resonance Fingerprinting"., , , , , , , and . IEEE Trans. Medical Imaging, 39 (2): 543 (2020)Masked Conditional Diffusion Model for Enhancing Deepfake Detection., , , , , , and . IJCNN, page 1-7. IEEE, (2024)What's in a Prior? Learned Proximal Networks for Inverse Problems., , and . CoRR, (2023)DeepSTI: Towards Tensor Reconstruction using Fewer Orientations in Susceptibility Tensor Imaging., , , , and . CoRR, (2022)RCA-U-Net: Residual Channel Attention U-Net for Fast Tissue Quantification in Magnetic Resonance Fingerprinting., , , , and . MICCAI (3), volume 11766 of Lecture Notes in Computer Science, page 101-109. Springer, (2019)CNS: CycleGAN-Assisted Neonatal Segmentation Model for Cross-Datasets., , , , , , , , , and 1 other author(s). GLMI@MICCAI, volume 11849 of Lecture Notes in Computer Science, page 172-179. Springer, (2019)What's in a Prior? Learned Proximal Networks for Inverse Problems., , and . ICLR, OpenReview.net, (2024)LMSA-Net: A lightweight multi-scale aware network for retinal vessel segmentation., , , and . Int. J. Imaging Syst. Technol., 33 (5): 1515-1530 (September 2023)