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

Oh, Yujin; Park, Sangjoon; Ye, Jong Chul

doi:10.1109/tmi.2020.2993291

[해외논문] Deep Learning COVID-19 Features on CXR Using Limited Training Data Sets 원문보기

IEEE transactions on medical imaging, v.39 no.8, 2020년, pp.2688 - 2700

Oh, Yujin (Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, South Korea) , Park, Sangjoon (Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, South Korea) , Ye, Jong Chul (Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, South Korea)

Abstract ▼ AI-Helper

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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