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

• URL: http://arxiv.org/abs/2004.05758v2
• Date: Tue, 5 May 2020 16:07:25 GMT
• Title: Deep Learning COVID-19 Features on CXR using Limited Training Data Sets
• Authors: Yujin Oh, Sangjoon Park, Jong Chul Ye
• Abstract summary: We propose a patch-based convolutional neural network approach with a relatively small number of trainable parameters for COVID-19 diagnosis. Experimental results show that our method achieves state-of-the-art performance and provides clinically interpretable saliency maps.
• Score: 40.45289250518209
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 the 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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