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10.1016/j.media.2020.101913

http://scihub22266oqcxt.onion/10.1016/j.media.2020.101913
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33285482!7689310!33285482
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suck abstract from ncbi


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pmid33285482      Med+Image+Anal 2021 ; 68 (ä): 101913
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  • COVID-AL: The diagnosis of COVID-19 with deep active learning #MMPMID33285482
  • Wu X; Chen C; Zhong M; Wang J; Shi J
  • Med Image Anal 2021[Feb]; 68 (ä): 101913 PMID33285482show ga
  • The efficient diagnosis of COVID-19 plays a key role in preventing the spread of this disease. The computer-aided diagnosis with deep learning methods can perform automatic detection of COVID-19 using CT scans. However, large scale annotation of CT scans is impossible because of limited time and heavy burden on the healthcare system. To meet the challenge, we propose a weakly-supervised deep active learning framework called COVID-AL to diagnose COVID-19 with CT scans and patient-level labels. The COVID-AL consists of the lung region segmentation with a 2D U-Net and the diagnosis of COVID-19 with a novel hybrid active learning strategy, which simultaneously considers sample diversity and predicted loss. With a tailor-designed 3D residual network, the proposed COVID-AL can diagnose COVID-19 efficiently and it is validated on a large CT scan dataset collected from the CC-CCII. The experimental results demonstrate that the proposed COVID-AL outperforms the state-of-the-art active learning approaches in the diagnosis of COVID-19. With only 30% of the labeled data, the COVID-AL achieves over 95% accuracy of the deep learning method using the whole dataset. The qualitative and quantitative analysis proves the effectiveness and efficiency of the proposed COVID-AL framework.
  • |*Deep Learning[MESH]
  • |*Tomography, X-Ray Computed[MESH]
  • |COVID-19/*diagnostic imaging[MESH]
  • |Datasets as Topic[MESH]
  • |Diagnosis, Computer-Assisted/*methods[MESH]
  • |Humans[MESH]
  • |Pneumonia, Viral/*diagnostic imaging/virology[MESH]


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