2 citations · 3 across the 6 of their papers we have counts for
6 papers
Self-supervised Noise2noise Method Utilizing Corrupted Images with a Modular Network for LDCT Denoising
Yuting Zhu, Qiang He, Yudong Yao +1
Deep learning is a very promising technique for low-dose computed tomography (LDCT) image denoising. However, traditional deep learning methods require paired noisy and clean datas…
NL-CS Net: Deep Learning with Non-Local Prior for Image Compressive Sensing
Shuai Bian, Shouliang Qi, Chen Li +2
Deep learning has been applied to compressive sensing (CS) of images successfully in recent years. However, existing network-based methods are often trained as the black box, in wh…
3D PETCT Tumor Lesion Segmentation via GCN Refinement
Hengzhi Xue, Qingqing Fang, Yudong Yao +1
Whole-body PET/CT scan is an important tool for diagnosing various malignancies (e.g., malignant melanoma, lymphoma, or lung cancer), and accurate segmentation of tumors is a key p…
QS-ADN: Quasi-Supervised Artifact Disentanglement Network for Low-Dose CT Image Denoising by Local Similarity Among Unpaired Data
Yuhui Ruan, Qiao Yuan, Chuang Niu +4
Deep learning has been successfully applied to low-dose CT (LDCT) image denoising for reducing potential radiation risk. However, the widely reported supervised LDCT denoising netw…
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…
Entropy Regularized Iterative Weighted Shrinkage-Thresholding Algorithm (ERIWSTA): An Application to CT Image Restoration
Bingxue Wu, Jiao Wei, Chen Li +2
The iterative weighted shrinkage-thresholding algorithm (IWSTA) has shown superiority to the classic unweighted iterative shrinkage-thresholding algorithm (ISTA) for solving linear…