The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
Deep Learning COVID-19 Features on CXR Using Limited Training Data Sets
Open accessAbstract
Under the global pandemic of COVID-19, the use of artificial intelligence to analyze chest X-ray (CXR) image for COVID-19 diagnosis and patient triage is becoming important. Unfortunately, due to the emergent nature of the COVID-19 pandemic, a systematic collection of CXR data set for deep neural network training is difficult. To address this problem, here we propose a patch-based convolutional neural network approach with a relatively small number of trainable parameters for COVID-19 diagnosis. The proposed method is inspired by our statistical analysis of the potential imaging biomarkers of the CXR radiographs. Experimental results show that our method achieves state-of-the-art performance and provides clinically interpretable saliency maps, which are useful for COVID-19 diagnosis and patient triage.
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Cites
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Machine learning: Algorithms, real-world applications and research directions
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Inf-Net: Automatic COVID-19 Lung Infection Segmentation From CT Images
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Liu, F., & Panagiotakos, D. (2022) · BMC Medical Research Methodology · doi:10.1186/s12874-022-01768-6 · Cited by 496
Nour, M., Cömert, Z., & Polat, K. (2020) · Applied Soft Computing · doi:10.1016/j.asoc.2020.106580 · Cited by 316
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Metadata
- Authors
- Oh, Y., Park, S., & Ye, J. C.
- Venue
- IEEE Transactions on Medical Imaging
- Published
- 2020-08
- Volume
- 39
- Issue
- 8
- Pages
- 2688-2700
- ISSN
- 0278-0062 (ISSN-L)
- 1558-254X
- OpenAlex
- W3016194608
- Cited by
- 897
- References
- 9
- FWCI
- 83.26
- Type
- journal article
- Language
- en
- Open access
- green
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