The DOI matches its Crossref record; 26 of the 26 references it cites were found in a registry or an index.
Dual-Sampling Attention Network for Diagnosis of COVID-19 From Community Acquired Pneumonia
Open accessAbstract
The coronavirus disease (COVID-19) is rapidly spreading all over the world, and has infected more than 1,436,000 people in more than 200 countries and territories as of April 9, 2020. Detecting COVID-19 at early stage is essential to deliver proper healthcare to the patients and also to protect the uninfected population. To this end, we develop a dual-sampling attention network to automatically diagnose COVID-19 from the community acquired pneumonia (CAP) in chest computed tomography (CT). In particular, we propose a novel online attention module with a 3D convolutional network (CNN) to focus on the infection regions in lungs when making decisions of diagnoses. Note that there exists imbalanced distribution of the sizes of the infection regions between COVID-19 and CAP, partially due to fast progress of COVID-19 after symptom onset. Therefore, we develop a dual-sampling strategy to mitigate the imbalanced learning. Our method is evaluated (to our best knowledge) upon the largest multi-center CT data for COVID-19 from 8 hospitals. In the training-validation stage, we collect 2186 CT scans from 1588 patients for a 5-fold cross-validation. In the testing stage, we employ another independent large-scale testing dataset including 2796 CT scans from 2057 patients. Results show that our algorithm can identify the COVID-19 images with the area under the receiver operating characteristic curve (AUC) value of 0.944, accuracy of 87.5%, sensitivity of 86.9%, specificity of 90.1%, and F1-score of 82.0%. With this performance, the proposed algorithm could potentially aid radiologists with COVID-19 diagnosis from CAP, especially in the early stage of the COVID-19 outbreak.
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Cited by
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COVID-19 Detection Through Transfer Learning Using Multimodal Imaging Data
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Yang, D., Xu, Z., Li, W., Myronenko, A., Roth, H. R., Harmon, S., Xu, S., Turkbey, B., Turkbey, E., Wang, X., et al. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.101992 · Cited by 279
Applications of artificial intelligence in battling against covid-19: A literature review
Tayarani N., M.-H. (2021) · Chaos, Solitons & Fractals · doi:10.1016/j.chaos.2020.110338 · Cited by 210
Celard, P., Iglesias, E. L., Sorribes-Fdez, J. M., Romero, R., Vieira, A. S., & Borrajo, L. (2023) · Neural Computing and Applications · doi:10.1007/s00521-022-07953-4 · Cited by 188
Metadata
- Authors
- Ouyang, X., Huo, J., Xia, L., Shan, F., Liu, J., Mo, Z., Yan, F., Ding, Z., Yang, Q., Song, B., Shi, F., Yuan, H., Wei, Y., Cao, X., Gao, Y., Wu, D., Wang, Q., & Shen, D.
- Venue
- IEEE Transactions on Medical Imaging
- Published
- 2020-08
- Volume
- 39
- Issue
- 8
- Pages
- 2595-2605
- ISSN
- 0278-0062 (ISSN-L)
- 1558-254X
- OpenAlex
- W3027914507
- Cited by
- 377
- References
- 26
- FWCI
- 36.76
- Type
- journal article
- Language
- en
- Open access
- green
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- Ouyang, X., Huo, J., Xia, L., Shan, F., Liu, J., Mo, Z., Yan, F., Ding, Z., Yang, Q., Song, B., Shi, F., Yuan, H., Wei, Y., Cao, X., Gao, Y., Wu, D., Wang, Q., & Shen, D. (2020). Dual-Sampling Attention Network for Diagnosis of COVID-19 From Community Acquired Pneumonia. IEEE Transactions on Medical Imaging. https://doi.org/10.1109/tmi.2020.2995508
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