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

2020/04/13 by Oh, Yujin, Park, Sangjoon, Ye, Jong Chul · 3 citations
#Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Electrical engineering #Image and Video Processing (eess.IV) #Machine Learning (cs.LG) #Machine Learning (stat.ML) #electronic engineering #information engineering

paper · doi:10.48550/arxiv.2004.05758

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.

Cited by

• EIR: Enhanced Image Representations for Medical Report Generation
• MedicalPatchNet: A Patch-Based Self-Explainable AI Architecture for Chest X-ray Classification
• A Guide to Robust Generalization: The Impact of Architecture, Pre-training, and Optimization Strategy

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