Search Results for author: Zhenghan Fang

Found 4 papers, 2 papers with code

Masked Conditional Diffusion Model for Enhancing Deepfake Detection

no code implementations1 Feb 2024 Tiewen Chen, Shanmin Yang, Shu Hu, Zhenghan Fang, Ying Fu, Xi Wu, Xin Wang

this paper present we put a new insight into diffusion model-based data augmentation, and propose a Masked Conditional Diffusion Model (MCDM) for enhancing deepfake detection.

Data Augmentation DeepFake Detection +1

What's in a Prior? Learned Proximal Networks for Inverse Problems

1 code implementation22 Oct 2023 Zhenghan Fang, Sam Buchanan, Jeremias Sulam

Proximal operators are ubiquitous in inverse problems, commonly appearing as part of algorithmic strategies to regularize problems that are otherwise ill-posed.

DeepSTI: Towards Tensor Reconstruction using Fewer Orientations in Susceptibility Tensor Imaging

no code implementations9 Sep 2022 Zhenghan Fang, Kuo-Wei Lai, Peter van Zijl, Xu Li, Jeremias Sulam

Experimental results using both simulation and in vivo human data demonstrate great improvement over state-of-the-art algorithms in terms of the reconstructed tensor image, principal eigenvector maps and tractography results, while allowing for tensor reconstruction with MR phase measured at much less than six different orientations.

Image Reconstruction

Artificial Intelligence Distinguishes COVID-19 from Community Acquired Pneumonia on Chest CT

1 code implementation Radiology 2020 Lin Li, Lixin Qin, Zeguo Xu, Youbing Yin, Xin Wang, Bin Kong, Junjie Bai, Yi Lu, Zhenghan Fang, Qi Song, Kunlin Cao, Daliang Liu, Guisheng Wang, Qizhong Xu, Xisheng Fang, Shiqin Zhang, Juan Xia, Jun Xia

Materials and Methods In this retrospective and multi-center study, a deep learning model, COVID-19 detection neural network (COVNet), was developed to extract visual features from volumetric chest CT exams for the detection of COVID-19.

COVID-19 Image Segmentation Specificity

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