J
Jun Xia
Researcher at Shenzhen University
Publications - 64
Citations - 2907
Jun Xia is an academic researcher from Shenzhen University. The author has contributed to research in topics: Medicine & Internal medicine. The author has an hindex of 12, co-authored 43 publications receiving 1537 citations. Previous affiliations of Jun Xia include Huazhong University of Science and Technology.
Papers
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Journal ArticleDOI
Using Artificial Intelligence to Detect COVID-19 and Community-acquired Pneumonia Based on Pulmonary CT: Evaluation of the Diagnostic Accuracy.
Lin Li,Lixin Qin,Zeguo Xu,Youbing Yin,Xin Wang,Bin Kong,Junjie Bai,Lu Yi,Zhenghan Fang,Qi Song,Kunlin Cao,Daliang Liu,Guisheng Wang,Qi-Zhong Xu,Xisheng Fang,Shiqin Zhang,Juan Xia,Jun Xia +17 more
TL;DR: A deep learning model was developed to extract visual features from volumetric chest CT scans for the detection of coronavirus 2019 and differentiate it from community-acquired pneumonia and other lung conditions.
Journal ArticleDOI
Weakly Supervised Deep Learning for COVID-19 Infection Detection and Classification From CT Images
Shaoping Hu,Yuan Gao,Zhangming Niu,Jiang Yinghui,Li Lao,Xianglu Xiao,Minhao Wang,Evandro Fei Fang,Wade Menpes-Smith,Jun Xia,Hui Ye,Guang Yang +11 more
TL;DR: This study proposes a weakly supervised deep learning strategy for detecting and classifying COVID-19 infection from CT images that can minimise the requirements of manual labelling of CT images but still be able to obtain accurate infection detection and distinguish CO VID-19 from non-COVID- 19 cases.
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Unbox the black-box for the medical explainable AI via multi-modal and multi-centre data fusion: A mini-review, two showcases and beyond
Guang Yang,Pazilova Nasibaxon Muhammadqosimovna, Rasulov Fayzulla,Mirzayev G'iyosbek Isroil o'g'li,Qinghao Ye,Jun Xia +4 more
TL;DR: This study surveyed the current progress of XAI and in particular its advances in healthcare applications, and introduced the solutions for XAI leveraging multi-modal and multi-centre data fusion, and subsequently validated in two showcases following real clinical scenarios.
Journal ArticleDOI
Precise diagnosis of intracranial hemorrhage and subtypes using a three-dimensional joint convolutional and recurrent neural network.
Hai Ye,Feng Gao,Youbing Yin,Danfeng Guo,Pengfei Zhao,Lu Yi,Xin Wang,Junjie Bai,Kunlin Cao,Qi Song,Heye Zhang,Wei Chen,Xuejun Guo,Jun Xia +13 more
TL;DR: The proposed CNN-RNN deep learning framework was able to accurately detect ICH and its subtypes with fast speed, suggesting its potential for assisting radiologists and physicians in their clinical diagnosis workflow.
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A single-center, retrospective study of COVID-19 features in children: a descriptive investigation.
Huijing Ma,Jiani Hu,Jie Tian,Xi Zhou,Hui Li,Maxwell T. Laws,Luke D Wesemann,Baiqi Zhu,Wei Chen,Wei Chen,Rafael Ramos,Jun Xia,Jianbo Shao +12 more
TL;DR: CT is a powerful tool to detect and characterize COVID-19 pneumonia but has little utility in evaluating clinical recovery for children, as one requirement is that pulmonary imaging must show significant lesion absorption prior to discharge.