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COVID-19 public CXR dataset

About COVID-19 public CXR dataset used in out recent paper

We provide the links for COVID-19 public datasets, which are used in our recent publication, "Deep Learning COVID-19 on CXR using Limited Training Data Sets".
https://ieeexplore.ieee.org/document/9090149

These public CXR datasets are available without any restrction by downloading in below links.​

[19] JSRT                http://db.jsrt.or.jp/eng.php
[20] SCR                 https://www.isi.uu.nl/Research/Databases/SCR/download.php
[21] NLM(MC)        http://academictorrents.com/details/ac786f74878a5775c81d490b23842fd4736bfe33
[22] Pneumonia     https://www.kaggle.com/praveengovi/coronahack-chest-xraydataset
[23] COVID-19        https://github.com/ieee8023/covid-chestxray-dataset

The metadata is also available in our github repository.
https://github.com/jongcye/Deep-Learning-COVID-19-on-CXR-using-Limited-Training-Data-Sets/blob/master/metadata.xls

To cite this,
 
@ARTICLE{9090149, author={Y. {Oh} and S. {Park} and J. C. {Ye}}, journal={IEEE Transactions on Medical Imaging}, title={Deep Learning COVID-19 Features on CXR using Limited Training Data Sets}, year={2020}, volume={}, number={}, pages={1-1},}
Link to article

ABOUT US
Our research activities are primarily focused on the signal processing and machine learning  for high-resolution high-sensitivity image reconstruction from real world bomedical imaging systems. Such problems pose interesting challenges that often lead to investigations of fundamental problems in various branches of physics, mathematics, signal processing, biology, and medicine. While most of the biomedical imaging researchers are interested in addressing this problem using off-the-self tools from signal processing, machine learning, statistics, and optimization and combining their domain-specific knowledge, our approaches are unique in the sense that I believe that actual  imaging applications are a source of endless inspiration for new mathematical theories and we are eager to solve both specific applications and application-inspired fundamental theoretical problems. 
Our location
Graduate School of AI
KAIST
108  Taebong-ro, Seocho-gu,  Seoul,  06764
Republic of Korea


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