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

By Yujin Oh, Sangjoon Park, Jong Chul Ye. IEEE Transactions on Medical Imaging, 2020

764 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/fedd928db5fbfa07819caacf01debbccca353a19

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Ling Shao
2. Sanja Fidler
3. Antonio Torralba
4. Deng-Ping Fan
5. Karsten Kreis
6. Jianbing Shen
7. Daiqing Li
8. Ge-Peng Ji

In Computer Vision and Pattern Recognition

1. Ling Shao
2. Deng-Ping Fan
3. Jianbing Shen
4. Ming-Ming Cheng
5. Wenguan Wang
6. Huan Fu
7. Luc Van Gool
8. Ge-Peng Ji

In Molecular Biology

1. Antonio Torralba
2. Huan Fu
3. Fatemeh Haghighat
4. Karsten Kreis
5. Sanja Fidler

Citations in the corpus

• Predicting the trend of indicators related to Covid-19 using the combined MLP-MC model (2021, positive): “Since the time series used in this study are also nonlinear and irregular, so it can be said that the results of this study are consistent with previous studies.”
• Semantic Segmentation with Generative Models: Semi-Supervised Learning and Strong Out-of-Domain Generalization (2021, methodology): “We follow [22] using histogram equalization and gamma correction as preprocessing steps.”
• Inf-Net: Automatic COVID-19 Lung Infection Segmentation From CT Images (2020, methodology): “Artificial intelligence technologies have been employed in a large number of applications against COVID-19 [6], [46]– [48].”