# Deep Learning COVID-19 Features on CXR using Limited Training Data Sets Source: [Deep Learning COVID-19 Features on CXR using Limited Training Data Sets]() Collection index: [llms.txt]() Data updated: 2025-10-27T06:02:46.151Z Authors: Yujin Oh, Sangjoon Park, Jong Chul Ye Published: 2020-04-13T03:44:42.000Z ## 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [COVID-19 Mortality Prediction on SnapCXR dataset (test)](): COVID-19 Mortality Prediction; SnapCXR dataset (test); AUC: 78. - [COVID-19 Ventilation Prediction on SnapCXR (test)](): COVID-19 Ventilation Prediction; SnapCXR (test); AUC: 0.75. ## Original sources and implementation [Original paper]() [PDF]()