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Wei Xia
Researcher at Chinese Academy of Sciences
Publications - 55
Citations - 841
Wei Xia is an academic researcher from Chinese Academy of Sciences. The author has contributed to research in topics: Medicine & Population. The author has an hindex of 11, co-authored 45 publications receiving 339 citations. Previous affiliations of Wei Xia include Fudan University.
Papers
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Journal ArticleDOI
Can peritumoral radiomics increase the efficiency of the prediction for lymph node metastasis in clinical stage T1 lung adenocarcinoma on CT
Xiang Wang,Xingyu Zhao,Xingyu Zhao,Qiong Li,Wei Xia,Zhaohui Peng,Rui Zhang,Qingchu Li,Junming Jian,Junming Jian,Wei Wang,Yuguo Tang,Shiyuan Liu,Xin Gao +13 more
TL;DR: Radiomics from peritumoral lung parenchyma increase the efficiency of the prediction for lymph node metastasis in clinical stage T1 lung adenocarcinoma on CT and radiomic signatures of GTV and PTV both had a good prediction ability in the prediction of LN metastasis.
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Preoperative radiomic signature based on multiparametric magnetic resonance imaging for noninvasive evaluation of biological characteristics in rectal cancer.
Xiaochun Meng,Wei Xia,Peiyi Xie,Rui Zhang,Wenru Li,Mengmeng Wang,Fei Xiong,Liu Yangchuan,Xinjuan Fan,Yao Xie,Xiangbo Wan,Kangshun Zhu,Hong Shan,Lei Wang,Xin Gao +14 more
TL;DR: Radiomic signatures based on MP-MRI have potential to noninvasively evaluate the biological characteristics of rectal cancer.
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MSFCN-multiple supervised fully convolutional networks for the osteosarcoma segmentation of CT images
TL;DR: The results indicated that the proposed algorithm contributed to the fast and accurate delineation of tumor boundaries, which could potentially assist doctors in making more precise treatment plans.
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Multiple supervised residual network for osteosarcoma segmentation in CT images
TL;DR: A multiple supervised residual network (MSRN) was proposed for osteosarcoma image segmentation and indicated that MSRN could give more accurate results than FCN and U-Net.
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Deep learning-based fully automated detection and segmentation of lymph nodes on multiparametric-mri for rectal cancer: A multicentre study.
Xingyu Zhao,Xingyu Zhao,Peiyi Xie,Mengmeng Wang,Mengmeng Wang,Wenru Li,Perry J. Pickhardt,Wei Xia,Fei Xiong,Rui Zhang,Yao Xie,Junming Jian,Junming Jian,Honglin Bai,Honglin Bai,Cai-Fang Ni,Jinhui Gu,Tao Yu,Yuguo Tang,Xin Gao,Xiaochun Meng +20 more
TL;DR: This deep learning–based, fully-automated lymph node detection and segmentation (auto-LNDS) model can achieve pelvic LNseffectively based on mpMRI for RC, and holds great potential for facilitating N-staging in clinical practice.