COVIDScreen: explainable deep learning framework for differential diagnosis of COVID-19 using chest X-rays
Author:
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Software
Link
http://link.springer.com/content/pdf/10.1007/s00521-020-05636-6.pdf
Reference96 articles.
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2. Adams HJ, Kwee TC, Yakar D, Hope MD, Kwee RM (2020) Chest CT imaging signature of coronavirus disease 2019 infection: in pursuit of the scientific evidence. Chest. https://doi.org/10.1016/j.chest.2020.06.025
3. Apostolopoulos ID, Mpesiana TA (2020) Covid-19: automatic detection from X-ray images utilizing transfer learning with convolutional neural networks. Phys Eng Sci Med 43(2):635–640. https://doi.org/10.1007/s13246-020-00865-4
4. Arons MM, Hatfield KM, Reddy SC, Kimball A, James A, Jacobs JR, Taylor J, Spicer K, Bardossy AC, Oakley LP, Tanwar S, Dyal JW, Harney J, Chisty Z, Bell JM, Methner M, Paul P, Carlson CM, McLaughlin HP, Thornburg N, Tong S, Tamin A, Tao Y, Uehara A, Harcourt J, Clark S, Brostrom-Smith C, Page LC, Kay M, Lewis J, Montgomery P, Stone ND, Clark TA, Honein MA, Duchin JS, Jernigan JA (2020) Presymptomatic sars-cov-2 infections and transmission in a skilled nursing facility. New Engl J Med 382(22):2081–2090. https://doi.org/10.1056/NEJMoa2008457
5. Bar Y, Diamant I, Wolf L, Greenspan H (2015) Deep learning with non-medical training used for chest pathology identification. In: Medical imaging 2015: computer-aided diagnosis, international society for optics and photonics, SPIE, vol 9414, pp 215–221. https://doi.org/10.1117/12.2083124
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