@misc{https://doi.org/10.5281/zenodo.3789644,<br/><span class="tab"></span>
  doi = {10.5281/ZENODO.3789644},<br/><span class="tab"></span>
  url = {https://zenodo.org/record/3789644},<br/><span class="tab"></span>
  author = {Ma,<br/><span class="tab"></span> Jun and Wang,<br/><span class="tab"></span> Yixin and An,<br/><span class="tab"></span> Xingle and Ge,<br/><span class="tab"></span> Cheng and Yu,<br/><span class="tab"></span> Ziqi},<br/><span class="tab"></span>
  keywords = {COVID-19,<br/><span class="tab"></span> Segmentation,<br/><span class="tab"></span> Lung,<br/><span class="tab"></span> Infection,<br/><span class="tab"></span> Deep Learning,<br/><span class="tab"></span> Convolutional Neural Network},<br/><span class="tab"></span>
  title = {Pretrained 3D U-Net models for COVID-19 CT Lung and Infection Segmentation},<br/><span class="tab"></span>
  publisher = {Zenodo},<br/><span class="tab"></span>
  year = {2020},<br/><span class="tab"></span>
  copyright = {Creative Commons Attribution 4.0 International}
}