239 citations · 339 across the 13 of their papers we have counts for
13 papers
Transformer-based Dual-domain Network for Few-view Dedicated Cardiac SPECT Image Reconstructions
Huidong Xie, Bo Zhou, Xiongchao Chen +6
Cardiovascular disease (CVD) is the leading cause of death worldwide, and myocardial perfusion imaging using SPECT has been widely used in the diagnosis of CVDs. The GE 530/570c de…
JCCS-PFGM: A Novel Circle-Supervision based Poisson Flow Generative Model for Multiphase CECT Progressive Low-Dose Reconstruction with Joint Condition
Rongjun Ge, Yuting He, Cong Xia +3
Multiphase contrast-enhanced computed tomography (CECT) scan is clinically significant to demonstrate the anatomy at different phases. In practice, such a multiphase CECT scan inhe…
Improving CT Image Segmentation Accuracy Using StyleGAN Driven Data Augmentation
Soham Bhosale, Arjun Krishna, Ge Wang +1
Medical Image Segmentation is a useful application for medical image analysis including detecting diseases and abnormalities in imaging modalities such as MRI, CT etc. Deep learnin…
Task-based Assessment of Deep Networks for Sinogram Denoising with A Transformer-based Observer
Yongyi Shi, Ge Wang, Xuanqin Mou
A variety of supervise learning methods are available for low-dose CT denoising in the sinogram domain. Traditional model observers are widely employed to evaluate these methods. H…
DiffusionCT: Latent Diffusion Model for CT Image Standardization
Md Selim, Jie Zhang, Michael A. Brooks +2
Computed tomography (CT) is one of the modalities for effective lung cancer screening, diagnosis, treatment, and prognosis. The features extracted from CT images are now used to qu…
Enabling Competitive Performance of Medical Imaging with Diffusion Model-generated Images without Privacy Leakage
Yongyi Shi, Wenjun Xia, Chuang Niu +2
Deep learning methods have impacted almost every research field, demonstrating notable successes in medical imaging tasks such as denoising and super-resolution. However, the prere…
Patch-Based Denoising Diffusion Probabilistic Model for Sparse-View CT Reconstruction
Wenjun Xia, Wenxiang Cong, Ge Wang
Sparse-view computed tomography (CT) can be used to reduce radiation dose greatly but is suffers from severe image artifacts. Recently, the deep learning based method for sparse-vi…
Low-Dose CT Using Denoising Diffusion Probabilistic Model for 20 Speedup
Wenjun Xia, Qing Lyu, Ge Wang
Low-dose computed tomography (LDCT) is an important topic in the field of radiology over the past decades. LDCT reduces ionizing radiation-induced patient health risks but it also…
Conversion Between CT and MRI Images Using Diffusion and Score-Matching Models
Qing Lyu, Ge Wang
MRI and CT are most widely used medical imaging modalities. It is often necessary to acquire multi-modality images for diagnosis and treatment such as radiotherapy planning. Howeve…
Quasi-supervised Learning for Super-resolution PET
Guangtong Yang, Chen Li, Yudong Yao +2
Low resolution of positron emission tomography (PET) limits its diagnostic performance. Deep learning has been successfully applied to achieve super-resolution PET. However, common…
Decorrelative Network Architecture for Robust Electrocardiogram Classification
Christopher Wiedeman, Ge Wang
Artificial intelligence has made great progress in medical data analysis, but the lack of robustness and trustworthiness has kept these methods from being widely deployed. As it is…
Segmentation-free PVC for Cardiac SPECT using a Densely-connected Multi-dimensional Dynamic Network
Huidong Xie, Zhao Liu, Luyao Shi +10
In nuclear imaging, limited resolution causes partial volume effects (PVEs) that affect image sharpness and quantitative accuracy. Partial volume correction (PVC) methods incorpora…
GasHis-Transformer: A Multi-scale Visual Transformer Approach for Gastric Histopathological Image Detection
Haoyuan Chen, Chen Li, Ge Wang +9
In this paper, a multi-scale visual transformer model, referred as GasHis-Transformer, is proposed for Gastric Histopathological Image Detection (GHID), which enables the automatic…