Deep Learning COVID-19 Features on CXR Using Limited Training Data Sets

Cited 2 time in webofscience Cited 0 time in scopus
  • Hit : 38
  • Download : 18
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.
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Issue Date
2020-08
Language
English
Article Type
Article
Citation

IEEE TRANSACTIONS ON MEDICAL IMAGING, v.39, no.8, pp.2688 - 2700

ISSN
0278-0062
DOI
10.1109/TMI.2020.2993291
URI
http://hdl.handle.net/10203/275915
Appears in Collection
BiS-Journal Papers(저널논문)
Files in This Item
000554893500012.pdf(3.96 MB)Download
This item is cited by other documents in WoS
⊙ Detail Information in WoSⓡ Click to see webofscience_button
⊙ Cited 2 items in WoS Click to see citing articles in records_button

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0