A Deep Learning System to Screen Novel Coronavirus Disease 2019 Pneumonia

[1]  Wei Wu,et al.  A deep learning system that generates quantitative CT reports for diagnosing pulmonary Tuberculosis , 2019, Appl. Intell..

[2]  Pu Liao,et al.  Antibody responses to SARS-CoV-2 in COVID-19 patients: the perspective application of serological tests in clinical practice , 2020, medRxiv.

[3]  R. Lu,et al.  Detection of SARS-CoV-2 in Different Types of Clinical Specimens. , 2020, JAMA.

[4]  Heshui Shi,et al.  Radiological findings from 81 patients with COVID-19 pneumonia in Wuhan, China: a descriptive study , 2020, The Lancet Infectious Diseases.

[5]  J. Kanne,et al.  Chest CT Findings in 2019 Novel Coronavirus (2019-nCoV) Infections from Wuhan, China: Key Points for the Radiologist , 2020, Radiology.

[6]  Z. Fayad,et al.  CT Imaging Features of 2019 Novel Coronavirus (2019-nCoV) , 2020, Radiology.

[7]  S. Lindstrom,et al.  First Case of 2019 Novel Coronavirus in the United States , 2020, The New England journal of medicine.

[8]  Ting Yu,et al.  Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study , 2020, The Lancet.

[9]  G. Gao,et al.  A Novel Coronavirus from Patients with Pneumonia in China, 2019 , 2020, The New England journal of medicine.

[10]  P. Horby,et al.  A novel coronavirus outbreak of global health concern , 2020, The Lancet.

[11]  S. Lo,et al.  A familial cluster of pneumonia associated with the 2019 novel coronavirus indicating person-to-person transmission: a study of a family cluster , 2020, The Lancet.

[12]  Y. Hu,et al.  Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China , 2020, The Lancet.

[13]  Dennis Normile,et al.  New SARS-like virus in China triggers alarm. , 2020, Science.

[14]  Michael J. Loeffelholz,et al.  Laboratory diagnosis of emerging human coronavirus infections – the state of the art , 2020, Emerging microbes & infections.

[15]  Wei Zhang,et al.  Molecular and serological investigation of 2019-nCoV infected patients: implication of multiple shedding routes , 2020, Emerging microbes & infections.

[16]  Victor M Corman,et al.  Detection of 2019 novel coronavirus (2019-nCoV) by real-time RT-PCR , 2020, Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin.

[17]  Qun Li.  Early Transmission Dynamics in Wuhan, China, of Novel Coronavirus–Infected Pneumonia , 2020 .

[18]  Xiangjian He,et al.  Deep Learning Techniques for Medical Image Segmentation: Achievements and Challenges , 2019, Journal of Digital Imaging.

[19]  Jian Zhou,et al.  Correction to: Deep learning reconstruction improves image quality of abdominal ultra-high-resolution CT , 2019, European Radiology.

[20]  G. Corrado,et al.  End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography , 2019, Nature Medicine.

[21]  Jian Zhou,et al.  Deep learning reconstruction improves image quality of abdominal ultra-high-resolution CT , 2019, European Radiology.

[22]  Xiaohui Xie,et al.  AnatomyNet: Deep learning for fast and fully automated whole‐volume segmentation of head and neck anatomy , 2018, Medical physics.

[23]  2018 9th International Conference on Information Technology in Medicine and Education (ITME) , 2018 .

[24]  Haiyang Huang,et al.  AE-CNN Classification of Pulmonary Tuberculosis Based on CT Images , 2018, 2018 9th International Conference on Information Technology in Medicine and Education (ITME).

[25]  Raúl San José Estépar,et al.  Pulmonary Artery–Vein Classification in CT Images Using Deep Learning , 2018, IEEE Transactions on Medical Imaging.

[26]  Xiaoming Liu,et al.  Automatic Organ Segmentation for CT Scans Based on Super-Pixel and Convolutional Neural Networks , 2018, Journal of Digital Imaging.

[27]  P. Huang,et al.  Added Value of Computer-aided CT Image Features for Early Lung Cancer Diagnosis with Small Pulmonary Nodules: A Matched Case-Control Study. , 2018, Radiology.

[28]  Jonathan T. Barron,et al.  Deep bilateral learning for real-time image enhancement , 2017, ACM Trans. Graph..

[29]  P. Lakhani,et al.  Deep Learning at Chest Radiography: Automated Classification of Pulmonary Tuberculosis by Using Convolutional Neural Networks. , 2017, Radiology.

[30]  Sebastian Thrun,et al.  Dermatologist-level classification of skin cancer with deep neural networks , 2017, Nature.

[31]  Sergey Ioffe,et al.  Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning , 2016, AAAI.

[32]  Thomas Brox,et al.  3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation , 2016, MICCAI.

[33]  Seyed-Ahmad Ahmadi,et al.  V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation , 2016, 2016 Fourth International Conference on 3D Vision (3DV).

[34]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[35]  Leo Breiman,et al.  Bagging Predictors , 1996, Machine Learning.

[36]  Marek J. Druzdzel,et al.  Learning Bayesian network parameters from small data sets: application of Noisy-OR gates , 2001, Int. J. Approx. Reason..