CovidCTNet: an open-source deep learning approach to diagnose covid-19 using small cohort of CT images.

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Title: CovidCTNet: an open-source deep learning approach to diagnose covid-19 using small cohort of CT images.
Authors: Javaheri T; Health Informatics Lab, Metropolitan College, Boston University, Boston, USA., Homayounfar M; Department of Biomedical Engineering, Amirkabir University of Technology, Tehran, Iran., Amoozgar Z; Department of Radiation Oncology, Massachusetts General Hospital, Harvard Medical School, Boston, USA., Reiazi R; Princess Margaret Cancer Centre, University of Toronto, Toronto, Canada.; Department of Medical Biophysics, University of Toronto, Toronto, Canada.; Department of Medical Physics, School of Medicine, Iran university of Medical Sciences, Tehran, Iran., Homayounieh F; Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, USA., Abbas E; Joint Department of Medical Imaging, University of Toronto, Toronto, Canada., Laali A; Department of Infectious Diseases, Firoozgar Hospital, Iran University of Medical Sciences, Tehran, Iran., Radmard AR; Department of Radiology, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran., Gharib MH; Department of Radiology and Golestan Rheumatology Research Center, Golestan University of Medical Sciences, Gorgan, Iran., Mousavi SAJ; Department of Internal Medicine, Iran University of Medical Sciences, Tehran, Iran., Ghaemi O; Department of Radiology, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran., Babaei R; Department of Radiology, Iran University of Medical Sciences, Tehran, Iran., Mobin HK; Department of Radiology, Iran University of Medical Sciences, Tehran, Iran., Hosseinzadeh M; Institute of Research and Development, Duy Tan University, Da Nang, Vietnam.; Health Management and Economics Research Center, Iran University of Medical Sciences, Tehran, Iran., Jahanban-Esfahlan R; Department of Medical Biotechnology, School of Advanced Medical Sciences, Tabriz University of Medical Sciences, Tabriz, Iran., Seidi K; Department of Medical Biotechnology, School of Advanced Medical Sciences, Tabriz University of Medical Sciences, Tabriz, Iran., Kalra MK; Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, USA., Zhang G; Health Informatics Lab, Metropolitan College, Boston University, Boston, USA.; Department of Computer Science, Metropolitan College, Boston University, Boston, USA., Chitkushev LT; Health Informatics Lab, Metropolitan College, Boston University, Boston, USA.; Department of Computer Science, Metropolitan College, Boston University, Boston, USA., Haibe-Kains B; Princess Margaret Cancer Centre, University of Toronto, Toronto, Canada.; Department of Medical Biophysics, University of Toronto, Toronto, Canada.; Department of Computer Science, University of Toronto, Toronto, ON, Canada.; Ontario Institute for Cancer Research, Toronto, ON, Canada.; Vector Institute for Artificial Intelligence, Toronto, ON, Canada., Malekzadeh R; Digestive Disease Research Center, Tehran University of Medical Sciences, Tehran, Iran., Rawassizadeh R; Health Informatics Lab, Metropolitan College, Boston University, Boston, USA. rezar@bu.edu.; Department of Computer Science, Metropolitan College, Boston University, Boston, USA. rezar@bu.edu.
Source: NPJ digital medicine [NPJ Digit Med] 2021 Feb 18; Vol. 4 (1), pp. 29. Date of Electronic Publication: 2021 Feb 18.
Publication Type: Journal Article
Journal Info: Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101731738 Publication Model: Electronic Cited Medium: Internet ISSN: 2398-6352 (Electronic) Linking ISSN: 23986352 NLM ISO Abbreviation: NPJ Digit Med Subsets: PubMed not MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:2398-6352
DOI:10.1038/s41746-021-00399-3