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10.1109/JBHI.2020.3042523

http://scihub22266oqcxt.onion/10.1109/JBHI.2020.3042523
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33275588!8545178!33275588
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suck abstract from ncbi


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pmid33275588      IEEE+J+Biomed+Health+Inform 2021 ; 25 (2): 441-452
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  • COVID-19 CT Image Synthesis With a Conditional Generative Adversarial Network #MMPMID33275588
  • Jiang Y; Chen H; Loew M; Ko H
  • IEEE J Biomed Health Inform 2021[Feb]; 25 (2): 441-452 PMID33275588show ga
  • Coronavirus disease 2019 (COVID-19) is an ongoing global pandemic that has spread rapidly since December 2019. Real-time reverse transcription polymerase chain reaction (rRT-PCR) and chest computed tomography (CT) imaging both play an important role in COVID-19 diagnosis. Chest CT imaging offers the benefits of quick reporting, a low cost, and high sensitivity for the detection of pulmonary infection. Recently, deep-learning-based computer vision methods have demonstrated great promise for use in medical imaging applications, including X-rays, magnetic resonance imaging, and CT imaging. However, training a deep-learning model requires large volumes of data, and medical staff faces a high risk when collecting COVID-19 CT data due to the high infectivity of the disease. Another issue is the lack of experts available for data labeling. In order to meet the data requirements for COVID-19 CT imaging, we propose a CT image synthesis approach based on a conditional generative adversarial network that can effectively generate high-quality and realistic COVID-19 CT images for use in deep-learning-based medical imaging tasks. Experimental results show that the proposed method outperforms other state-of-the-art image synthesis methods with the generated COVID-19 CT images and indicates promising for various machine learning applications including semantic segmentation and classification.
  • |*Deep Learning[MESH]
  • |*Tomography, X-Ray Computed[MESH]
  • |COVID-19/*diagnostic imaging[MESH]
  • |Humans[MESH]
  • |Lung/diagnostic imaging[MESH]
  • |Radiography, Thoracic[MESH]


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