Jun Ma
2020-10-12
<p>Trained models in the following paper.</p>
<p>@article{Ma20SemiCOVIDSeg,<br>
author={Jun Ma and Ziwei Nie and Congcong Wang and Guoqiang Dong and Qiongjie Zhu and Jian He and Luying Gui and Xiaoping Yang},<br>
title={Active contour regularized semi-supervised learning for COVID-19 CT infection segmentation with limited annotations},<br>
journal={Physics in Medicine & Biology},<br>
url={http://iopscience.iop.org/article/10.1088/1361-6560/abc04e},<br>
year={2020}<br>
}</p>
<p>These trained models were built on nnUNet (https://github.com/MIC-DKFZ/nnUNet).</p>
<p>We also use them to infer this public COVID-19 CT dataset:https://wiki.cancerimagingarchive.net/display/Public/CT+Images+in+COVID-19.</p>
<p>These pseudo labels are also provided.</p>
https://doi.org/10.1088/1361-6560/abc04e
oai:zenodo.org:4246238
eng
Zenodo
https://zenodo.org/communities/covid-19
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Physics in Medicine & Biology, (2020-10-12)
COVID-19, Semi-supervised Learning, Segentation, Lung, Infection
Trained Models of Semi-supervised COVID-19 Infection Segmentation
info:eu-repo/semantics/other