no code implementations • 23 Apr 2024 • Yuxiang Yang, Lu Wen, Yuanyuan Xu, Jiliu Zhou, Yan Wang
Universal Multi-source Domain Adaptation (UniMDA) transfers knowledge from multiple labeled source domains to an unlabeled target domain under domain shifts (different data distribution) and class shifts (unknown target classes).
no code implementations • 2 Apr 2024 • Yuchen Fei, Yanmei Luo, Yan Wang, Jiaqi Cui, Yuanyuan Xu, Jiliu Zhou, Dinggang Shen
In this paper, to reconstruct high-quality SPET images from multi-dose-level LPET images, we design a novel two-phase multi-dose-level PET reconstruction algorithm with dose level awareness, containing a pre-training phase and a SPET prediction phase.
no code implementations • 6 Mar 2024 • Lu Wen, Zhenghao Feng, Yun Hou, Peng Wang, Xi Wu, Jiliu Zhou, Yan Wang
Semi-supervised learning is a sound measure to relieve the strict demand of abundant annotated datasets, especially for challenging multi-organ segmentation .
no code implementations • 29 Feb 2024 • Jiaqi Cui, Yuanyuan Xu, Jianghong Xiao, Yuchen Fei, Jiliu Zhou, Xingcheng Peng, Yan Wang
Deep learning has facilitated the automation of radiotherapy by predicting accurate dose distribution maps.
no code implementations • 7 Feb 2024 • Lu Wen, Qihun Zhang, Zhenghao Feng, Yuanyuan Xu, Xiao Chen, Jiliu Zhou, Yan Wang
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 at risk (OARs).
1 code implementation • 1 Feb 2024 • Jiaqi Cui, Yan Wang, Lu Wen, Pinxian Zeng, Xi Wu, Jiliu Zhou, Dinggang Shen
To obtain high-quality Positron emission tomography (PET) images while minimizing radiation exposure, numerous methods have been proposed to reconstruct standard-dose PET (SPET) images from the corresponding low-dose PET (LPET) images.
no code implementations • 6 Nov 2023 • Zhenghao Feng, Lu Wen, Jianghong Xiao, Yuanyuan Xu, Xi Wu, Jiliu Zhou, Xingchen Peng, Yan Wang
In the forward process, DiffDose transforms dose distribution maps into pure Gaussian noise by gradually adding small noise and a noise predictor is simultaneously trained to estimate the noise added at each timestep.
1 code implementation • 20 Aug 2023 • Zeyu Han, YuHan Wang, Luping Zhou, Peng Wang, Binyu Yan, Jiliu Zhou, Yan Wang, Dinggang Shen
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-dose PET (SPET) images from low-dose PET (LPET) images.
no code implementations • 10 Aug 2023 • Jiaqi Cui, Pinxian Zeng, Xinyi Zeng, Peng Wang, Xi Wu, Jiliu Zhou, Yan Wang, Dinggang Shen
Specifically, the TriDo-Former consists of two cascaded networks, i. e., a sinogram enhancement transformer (SE-Former) for denoising the input LPET sinograms and a spatial-spectral reconstruction transformer (SSR-Former) for reconstructing SPET images from the denoised sinograms.
no code implementations • 19 Jul 2023 • Zhenghao Feng, Lu Wen, Peng Wang, Binyu Yan, Xi Wu, Jiliu Zhou, Yan Wang
To alleviate this limitation, we innovatively introduce a diffusion-based dose prediction (DiffDP) model for predicting the radiotherapy dose distribution of cancer patients.
no code implementations • ICCV 2023 • Qiankun Ma, Jiyao Gao, Bo Zhan, Yunpeng Guo, Jiliu Zhou, Yan Wang
Conventional semi-supervised learning (SSL) lies in the close-set assumption that the labeled and unlabeled sets contain data with the same seen classes, called in-distribution (ID) data.
no code implementations • 24 Nov 2021 • Jiahui Ni, Zhimin Shao, Zhongzhou Zhang, Mingzheng Hou, Jiliu Zhou, Leyuan Fang, Yi Zhang
In addition, a novel hybrid loss function is proposed to constrain both spatial and spectral consistency between the pansharpened image and the PAN and LRMS images at different resolutions.
no code implementations • 29 Oct 2021 • Chenyu Shen, Wenjun Xia, Hongwei Ye, Mingzheng Hou, Hu Chen, Yan Liu, Jiliu Zhou, Yi Zhang
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 imaging quality.
no code implementations • 14 May 2021 • Zhiwen Wang, Wenjun Xia, Zexin Lu, Yongqiang Huang, Yan Liu, Hu Chen, Jiliu Zhou, Yi Zhang
Magnetic resonance imaging (MRI) acquisition, reconstruction, and segmentation are usually processed independently in the conventional practice of MRI workflow.
no code implementations • 6 May 2021 • Yuchen Fei, Bo Zhan, Mei Hong, Xi Wu, Jiliu Zhou, Yan Wang
To take full advantage of the complementary information provided by different modalities, multi-modal MRI sequences are utilized as input.
no code implementations • 3 Apr 2021 • Tao Wang, Wenjun Xia, Zexin Lu, Huaiqiang Sun, Yan Liu, Hu Chen, Jiliu Zhou, Yi Zhang
Since the dual-domain MAR methods can leverage the hybrid information from both sinogram and image domains, they have significantly improved the performance compared to single-domain methods.
no code implementations • 24 Mar 2021 • Zexin Lu, Wenjun Xia, Yongqiang Huang, Hongming Shan, Hu Chen, Jiliu Zhou, Yi Zhang
Recent advance on neural network architecture search (NAS) has proved that the network architecture has a dramatic effect on the model performance, which indicates that current network architectures for LDCT may be sub-optimal.
no code implementations • 18 Mar 2021 • 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 based on its spectral-spatial characteristics.
1 code implementation • 16 Feb 2021 • Tao Wang, Wenjun Xia, Yongqiang Huang, Huaiqiang Sun, Yan Liu, Hu Chen, Jiliu Zhou, Yi Zhang
With the rapid development of deep learning in the field of medical imaging, several network models have been proposed for metal artifact reduction (MAR) in CT.
1 code implementation • 13 Dec 2020 • Yi Zhang, Hu Chen, Wenjun Xia, Yang Chen, Baodong Liu, Yan Liu, Huaiqiang Sun, Jiliu Zhou
Compressed sensing (CS) computed tomography has been proven to be important for several clinical applications, such as sparse-view computed tomography (CT), digital tomosynthesis and interior tomography.
no code implementations • 27 Oct 2020 • Wenjun Xia, Zexin Lu, Yongqiang Huang, Yan Liu, Hu Chen, Jiliu Zhou, Yi Zhang
Current mainstream of CT reconstruction methods based on deep learning usually needs to fix the scanning geometry and dose level, which will significantly aggravate the training cost and need more training data for clinical application.
no code implementations • 27 Oct 2020 • Xiang Chen, Wenjun Xia, Yan Liu, Hu Chen, Jiliu Zhou, Yi Zhang
Spectral computed tomography (CT) can reconstruct spectral images from different energy bins using photon counting detectors (PCDs).
no code implementations • 16 Oct 2020 • Yuang Shi, Chen Zu, Mei Hong, Luping Zhou, Lei Wang, Xi Wu, Jiliu Zhou, Daoqiang Zhang, Yan Wang
With the increasing amounts of high-dimensional heterogeneous data to be processed, multi-modality feature selection has become an important research direction in medical image analysis.
no code implementations • 14 Jun 2020 • Zerui Shao, Yi-Fei PU, Jiliu Zhou, Bihan Wen, Yi Zhang
Robust Principal Component Analysis (RPCA), as one of the most popular moving object modelling methods, aims to separate the temporally varying (i. e., moving) foreground objects from the static background in video, assuming the background frames to be low-rank while the foreground to be spatially sparse.
no code implementations • 11 Jun 2020 • Pin Tang, Chen Zu, Mei Hong, Rui Yan, Xingchen Peng, Jianghong Xiao, Xi Wu, Jiliu Zhou, Luping Zhou, Yan Wang
In this paper, we propose a Dense SegU-net (DSU-net) framework for automatic NPC segmentation in MRI.
no code implementations • MEd Phy 2019 • Zongqing Ma 1, Xi Wu 2, Xin Wang 3, Qi Song 3, Youbing Yin 3, Kunlin Cao 3, Yan Wang 1, Jiliu Zhou
Methods: To effectively leverage spatial context information, the proposed IMFCN explicitly models the interslice spatial correlations using a multi-path late fusion strategy.
no code implementations • 20 Mar 2019 • Peng Bao, Wenjun Xia, Kang Yang, Weiyan Chen, Mianyi Chen, Yan Xi, Shanzhou Niu, Jiliu Zhou, He Zhang, Huaiqiang Sun, Zhangyang Wang, Yi Zhang
Over the past few years, dictionary learning (DL)-based methods have been successfully used in various image reconstruction problems.
no code implementations • 15 Oct 2018 • Peng Bao, Wenjun Xia, Kang Yang, Jiliu Zhou, Yi Zhang
Traditional dictionary learning based CT reconstruction methods are patch-based and the features learned with these methods often contain shifted versions of the same features.
1 code implementation • 12 Aug 2018 • Maosong Ran, Jinrong Hu, Yang Chen, Hu Chen, Huaiqiang Sun, Jiliu Zhou, Yi Zhang
Structure-preserved denoising of 3D magnetic resonance imaging (MRI) images is a critical step in medical image analysis.
no code implementations • 30 Jul 2017 • Hu Chen, Yi Zhang, Yunjin Chen, Junfeng Zhang, Weihua Zhang, Huaiqiaing Sun, Yang Lv, Peixi Liao, Jiliu Zhou, Ge Wang
Compressive sensing (CS) has proved effective for tomographic reconstruction from sparsely collected data or under-sampled measurements, which are practically important for few-view CT, tomosynthesis, interior tomography, and so on.
1 code implementation • 1 Feb 2017 • Hu Chen, Yi Zhang, Mannudeep K. Kalra, Feng Lin, Yang Chen, Peixi Liao, Jiliu Zhou, Ge Wang
Given the potential X-ray radiation risk to the patient, low-dose CT has attracted a considerable interest in the medical imaging field.
no code implementations • 2 Oct 2016 • Hu Chen, Yi Zhang, Weihua Zhang, Peixi Liao, Ke Li, Jiliu Zhou, Ge Wang
To reduce the potential radiation risk, low-dose CT has attracted much attention.
no code implementations • 27 Sep 2016 • Hu Chen, Yi Zhang, Weihua Zhang, Peixi Liao, Ke Li, Jiliu Zhou, Ge Wang
In order to reduce the potential radiation risk, low-dose CT has attracted more and more attention.
Medical Physics