COVID-AL: The diagnosis of COVID-19 with deep active learning

Xing Wu*, Cheng Chen, Mingyu Zhong, Jianjia Wang, Jun Shi

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

90 Citations (Scopus)


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.

Original languageEnglish
Article number101913
JournalMedical Image Analysis
Publication statusPublished - Feb 2021
Externally publishedYes


  • COVID-19
  • Computer-aided diagnosis
  • Deep active learning
  • Predicted loss
  • Sample diversity


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