<div class="sc-kdrUpr eZtUed"><div class="sc-UEtKG dGqiYy sc-hDzlxo bEIZRR"><div class="sc-fqwslf gsqkEc"><div class="sc-cBQMlg kAHhUk"><h2 class="sc-dcKlJK sc-cVttbi gqEuPW ksnHgj">About Dataset</h2></div></div></div><div class="sc-fHzVOS cUYeeo"><div class="sc-davvxH nUNNB"><div style="min-height: 80px;"><div class="sc-etVRix bfyesi sc-jCNfQM igJSrG"><h1>Context</h1>

<p>This dataset was created to assist with completion of the following Kaggle competition, <br>

<a aria-label="https://www.kaggle.com/c/siim-covid19-detection/overview (opens in a new tab)" target="_blank" href="https://www.kaggle.com/c/siim-covid19-detection/overview">https://www.kaggle.com/c/siim-covid19-detection/overview</a></p>

<h1>Content</h1>

<p>This dataset consists of 60 xray images of the chest region, and a corresponding numpy file of labels. Each numpy label file, consists of a 4x(width)x(height), where the first dimension specifies <br>

lungs 0<br>

heart 1<br>

diaphragm 2<br>

spinal column 3<br>

, and the remaining dimensions correspond to the image pixel locations. A pixel is 0 if the corresponding body part was not present, and 1 if present.</p>

<p>This data set was manually created using <a rel="noreferrer nofollow" aria-label="https://github.com/wkentaro/labelme (opens in a new tab)" target="_blank" href="https://github.com/wkentaro/labelme">https://github.com/wkentaro/labelme</a> to draw associated shapes encapsulating each body part. </p>

<h1>Disclaimer (Caveat Emptor)</h1>

<p>This labeled portion of the dataset was created by non-medical affiliated students. This dataset is a start, and meant to inspire others to create a more extensive database of similar images. </p>

<h1>Citation</h1>

<p>The BIMCV-COVID19 Data used by this challenge were originally published by the Medical Imaging Databank of the Valencia Region (BIMCV) in cooperation with The Foundation for the Promotion of Health and Biomedical Research of Valencia Region (FISABIO), and the Regional Ministry of Innovation, Universities, Science and Digital Society (Generalitat Valenciana), however the images were completely re-annotated using different annotation types. Users of this data must abide by the BIMCV-COVID19 Dataset research Use Agreement. Paper Reference: BIMCV COVID-19+: a large annotated dataset of RX and CT images from COVID-19 patients</p>

<p>The MIDRC-RICORD Data used by this challenge were originally published by The Cancer Imaging Archive. The images were re-annotated for this challenge using a different annotation schema. Users of this data must abide by the TCIA Data Usage Policy and the Creative Commons Attribution-NonCommercial 4.0 International License under which it has been published. Attribution should include references to citations listed on the TCIA citation information page (page bottom). Paper Reference: The RSNA International COVID-19 Open Radiology Database (RICORD)<br>

Citations &amp; Data Usage Policy<br>

Users of this data must abide by the TCIA Data Usage Policy and the Creative Commons Attribution-NonCommercial 4.0 International License under which it has been published. Attribution should include references to the following citations:</p>

<p>Data Citation</p>

<p>Tsai, E., Simpson, S., Lungren, M.P., Hershman, M., Roshkovan, L., Colak, E., Erickson, B.J., Shih, G., Stein, A.,Kalpathy-Cramer, J., Shen, J.,Hafez, M.A.F., John, S., Rajiah, P., Pogatchnik, B.P., Mongan, J.T., Altinmakas, E., Ranschaert, E., Kitamura, F.C., Topff, L., Moy, L., Kanne, J.P., &amp; Wu, C. (2021). Data from Medical Imaging Data Resource Center (MIDRC) - RSNA International COVID Radiology Database (RICORD) Release 1c - Chest x-ray, Covid+ (MIDRC-RICORD-1c). The Cancer Imaging Archive.  DOI: <a rel="noreferrer nofollow" aria-label="https://doi.org/10.7937/91ah-v663 (opens in a new tab)" target="_blank" href="https://doi.org/10.7937/91ah-v663">https://doi.org/10.7937/91ah-v663</a>.</p>

<p>Publication Citation</p>

<p>Tsai, E. B., Simpson, S., Lungren, M., Hershman, M., Roshkovan, L., Colak, E., Erickson, B. J., Shih, G., Stein, A., Kalpathy-Cramer, J., Shen, J., Hafez, M., John, S., Rajiah, P., Pogatchnik, B. P., Mongan, J., Altinmakas, E., Ranschaert, E. R., Kitamura, F. C., … Wu, C. C. (2021). The RSNA International COVID-19 Open Annotated Radiology Database (RICORD). Radiology, 203957. DOI: <a rel="noreferrer nofollow" aria-label="https://doi.org/10.1148/radiol.2021203957 (opens in a new tab)" target="_blank" href="https://doi.org/10.1148/radiol.2021203957">https://doi.org/10.1148/radiol.2021203957</a></p>

<p>TCIA Citation</p>

<p>Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, Moore S, Phillips S, Maffitt D, Pringle M, Tarbox L, Prior F. The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository, Journal of Digital Imaging, Volume 26, Number 6, December, 2013, pp 1045-1057. DOI: 10.1007/s10278-013-9622-7</p>