<div class="sc-jegwdG lhLRCf"><div class="sc-UEtKG dGqiYy sc-flttKd cguEtd"><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-davvxH eCVTlP"><div class="sc-jCNfQM ikRdXB"><div style="min-height: 80px;"><div class="sc-etVRix jqYJaa sc-gVIFzB gQKGyV"><h3>Context</h3>

<p>When I started playing around with deep learning in radiology, the first barrier I faced was obtaining a dataset. So, I just downloaded some public images from google images.</p>

<h3>Content</h3>

<p>This dataset contains 100 normal head CT slices and 100 other with hemorrhage. No distinction between kinds of hemorrhage.<br>

Labels are on a CSV file. Each slice comes from a different person.<br>

The main idea of such a small dataset is to develop ways to predict imaging findings even in a context of little data.<br>

In <a aria-label="this notebook (opens in a new tab)" target="_blank" href="https://www.kaggle.com/felipekitamura/head-ct-hemorrhage-kernel">this notebook</a>, I present a simple data augmentation capable of achieving 90% accuracy in the test set.</p>

<h3>Acknowledgements</h3>

<p>Thanks for the people who made their images available on google.</p>

<h3>Inspiration</h3>

<p>Help push the frontiers of Artificial Intelligence in Medical Imaging.</p></div></div></div></div></div>