Please use this identifier to cite or link to this item: https://covid-19.conacyt.mx/jspui/handle/1000/1992
Weakly Supervised Deep Learning for COVID-19 Infection Detection and Classification from CT Images
Shaoping Hu.
Yuan Gao.
Zhangming Niu.
Yinghui Jiang.
Lao Li.
Xianglu Xiao.
Minhao Wang.
Evandro Fei Fang.
Wade Menpes-Smith.
Jun Xia.
Hui Ye.
Guang Yang.
Acceso Abierto
Atribución-NoComercial-SinDerivadas
https://arxiv.org/pdf/2004.06689v1.pdf
An outbreak of a novel coronavirus disease (i.e., COVID-19) has been recorded in Wuhan, China since late December 2019, which subsequently became pandemic around the world. Although COVID-19 is an acutely treated disease, it can also be fatal with a risk of fatality of 4.03% in China and the highest of 13.04% in Algeria and 12.67% Italy (as of 8th April 2020). The onset of serious illness may result in death as a consequence of substantial alveolar damage and progressive respiratory failure. Although laboratory testing, e.g., using reverse transcription polymerase chain reaction (RT-PCR), is the golden standard for clinical diagnosis, the tests may produce false negatives. Moreover, under the pandemic situation, shortage of RT-PCR testing resources may also delay the following clinical decision and treatment. Under such circumstances, chest CT imaging has become a valuable tool for both diagnosis and prognosis of COVID-19 patients. In this study, we propose a weakly supervised deep learning strategy for detecting and classifying COVID-19 infection from CT images. The proposed method can minimise the requirements of manual labelling of CT images but still be able to obtain accurate infection detection and distinguish COVID-19 from non-COVID-19 cases. Based on the promising results obtained qualitatively and quantitatively, we can envisage a wide deployment of our developed technique in large-scale clinical studies.
arxiv.org
2020
Artículo
https://arxiv.org/pdf/2004.06689v1.pdf
Inglés
VIRUS RESPIRATORIOS
Appears in Collections:Artículos científicos

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