1.9k citations · 2.4k across the 39 of their papers we have counts for
39 papers
S3PET: Semi-supervised Standard-dose PET Image Reconstruction via Dose-aware Token Swap
Jiaqi Cui, Pinxian Zeng, Yuanyuan Xu +3
To acquire high-quality positron emission tomography (PET) images while reducing the radiation tracer dose, numerous efforts have been devoted to reconstructing standard-dose PET (…
Learning with Alignments: Tackling the Inter- and Intra-domain Shifts for Cross-multidomain Facial Expression Recognition
Yuxiang Yang, Lu Wen, Xinyi Zeng +4
Facial Expression Recognition (FER) holds significant importance in human-computer interactions. Existing cross-domain FER methods often transfer knowledge solely from a single lab…
MCAD: Multi-modal Conditioned Adversarial Diffusion Model for High-Quality PET Image Reconstruction
Jiaqi Cui, Xinyi Zeng, Pinxian Zeng +4
Radiation hazards associated with standard-dose positron emission tomography (SPET) images remain a concern, whereas the quality of low-dose PET (LPET) images fails to meet clinica…
Adaptive Prompt Learning with Negative Textual Semantics and Uncertainty Modeling for Universal Multi-Source Domain Adaptation
Yuxiang Yang, Lu Wen, Yuanyuan Xu +2
Universal Multi-source Domain Adaptation (UniMDA) transfers knowledge from multiple labeled source domains to an unlabeled target domain under domain shifts (different data distrib…
Two-Phase Multi-Dose-Level PET Image Reconstruction with Dose Level Awareness
Yuchen Fei, Yanmei Luo, Yan Wang +4
To obtain high-quality positron emission tomography (PET) while minimizing radiation exposure, a range of methods have been designed to reconstruct standard-dose PET (SPET) from co…
Dcl-Net: Dual Contrastive Learning Network for Semi-Supervised Multi-Organ Segmentation
Lu Wen, Zhenghao Feng, Yun Hou +4
Semi-supervised learning is a sound measure to relieve the strict demand of abundant annotated datasets, especially for challenging multi-organ segmentation . However, most existin…
Dose Prediction Driven Radiotherapy Paramters Regression via Intra- and Inter-Relation Modeling
Jiaqi Cui, Yuanyuan Xu, Jianghong Xiao +4
Deep learning has facilitated the automation of radiotherapy by predicting accurate dose distribution maps. However, existing methods fail to derive the desirable radiotherapy para…
Triplet-constraint Transformer with Multi-scale Refinement for Dose Prediction in Radiotherapy
Lu Wen, Qihun Zhang, Zhenghao Feng +4
Radiotherapy is a primary treatment for cancers with the aim of applying sufficient radiation dose to the planning target volume (PTV) while minimizing dose hazards to the organs a…
Image2Points:A 3D Point-based Context Clusters GAN for High-Quality PET Image Reconstruction
Jiaqi Cui, Yan Wang, Lu Wen +4
To obtain high-quality Positron emission tomography (PET) images while minimizing radiation exposure, numerous methods have been proposed to reconstruct standard-dose PET (SPET) im…
Diffusion-based Radiotherapy Dose Prediction Guided by Inter-slice Aware Structure Encoding
Zhenghao Feng, Lu Wen, Jianghong Xiao +5
Deep learning (DL) has successfully automated dose distribution prediction in radiotherapy planning, enhancing both efficiency and quality. However, existing methods suffer from th…
Contrastive Diffusion Model with Auxiliary Guidance for Coarse-to-Fine PET Reconstruction
Zeyu Han, Yuhan Wang, Luping Zhou +5
To obtain high-quality positron emission tomography (PET) scans while reducing radiation exposure to the human body, various approaches have been proposed to reconstruct standard-d…
TriDo-Former: A Triple-Domain Transformer for Direct PET Reconstruction from Low-Dose Sinograms
Jiaqi Cui, Pinxian Zeng, Xinyi Zeng +5
To obtain high-quality positron emission tomography (PET) images while minimizing radiation exposure, various methods have been proposed for reconstructing standard-dose PET (SPET)…
DiffDP: Radiotherapy Dose Prediction via a Diffusion Model
Zhenghao Feng, Lu Wen, Peng Wang +4
Currently, deep learning (DL) has achieved the automatic prediction of dose distribution in radiotherapy planning, enhancing its efficiency and quality. However, existing methods s…
SOUL-Net: A Sparse and Low-Rank Unrolling Network for Spectral CT Image Reconstruction
Xiang Chen, Wenjun Xia, Ziyuan Yang +4
Spectral computed tomography (CT) is an emerging technology, that generates a multienergy attenuation map for the interior of an object and extends the traditional image volume int…
LDP-Net: An Unsupervised Pansharpening Network Based on Learnable Degradation Processes
Jiahui Ni, Zhimin Shao, Zhongzhou Zhang +4
Pansharpening in remote sensing image aims at acquiring a high-resolution multispectral (HRMS) image directly by fusing a low-resolution multispectral (LRMS) image with a panchroma…
Unsupervised PET Reconstruction from a Bayesian Perspective
Chenyu Shen, Wenjun Xia, Hongwei Ye +5
Positron emission tomography (PET) reconstruction has become an ill-posed inverse problem due to low-count projection data, and a robust algorithm is urgently required to improve i…
One Network to Solve Them All: A Sequential Multi-Task Joint Learning Network Framework for MR Imaging Pipeline
Zhiwen Wang, Wenjun Xia, Zexin Lu +5
Magnetic resonance imaging (MRI) acquisition, reconstruction, and segmentation are usually processed independently in the conventional practice of MRI workflow. It is easy to notic…
Deep Learning based Multi-modal Computing with Feature Disentanglement for MRI Image Synthesis
Yuchen Fei, Bo Zhan, Mei Hong +3
Purpose: Different Magnetic resonance imaging (MRI) modalities of the same anatomical structure are required to present different pathological information from the physical level f…
IDOL-Net: An Interactive Dual-Domain Parallel Network for CT Metal Artifact Reduction
Tao Wang, Wenjun Xia, Zexin Lu +5
Due to the presence of metallic implants, the imaging quality of computed tomography (CT) would be heavily degraded. With the rapid development of deep learning, several network mo…
MANAS: Multi-Scale and Multi-Level Neural Architecture Search for Low-Dose CT Denoising
Zexin Lu, Wenjun Xia, Yongqiang Huang +4
Lowering the radiation dose in computed tomography (CT) can greatly reduce the potential risk to public health. However, the reconstructed images from the dose-reduced CT or low-do…
TPPI-Net: Towards Efficient and Practical Hyperspectral Image Classification
Hao Chen, Xiaohua Li, Jiliu Zhou
Hyperspectral Image(HSI) classification is the most vibrant field of research in the hyperspectral community, which aims to assign each pixel in the image to one certain category b…
DAN-Net: Dual-Domain Adaptive-Scaling Non-local Network for CT Metal Artifact Reduction
Tao Wang, Wenjun Xia, Yongqiang Huang +5
Metal implants can heavily attenuate X-rays in computed tomography (CT) scans, leading to severe artifacts in reconstructed images, which significantly jeopardize image quality and…
LEARN++: Recurrent Dual-Domain Reconstruction Network for Compressed Sensing CT
Yi Zhang, Hu Chen, Wenjun Xia +5
Compressed sensing (CS) computed tomography has been proven to be important for several clinical applications, such as sparse-view computed tomography (CT), digital tomosynthesis a…
Fourth-Order Nonlocal Tensor Decomposition Model for Spectral Computed Tomography
Xiang Chen, Wenjun Xia, Yan Liu +3
Spectral computed tomography (CT) can reconstruct spectral images from different energy bins using photon counting detectors (PCDs). However, due to the limited photons and countin…