A Weakly Supervised Consistency-based Learning Method for COVID-19 Segmentation in CT Images. Laradji, I. H., Rodríguez, P., Mañas, O., Lensink, K., Law, M., Kurzman, L., Parker, W., Vázquez, D., & Nowrouzezahrai, D. In IEEE Winter Conference on Applications of Computer Vision, WACV 2021, Waikoloa, HI, USA, January 3-8, 2021, pages 2452–2461, 2021. IEEE.
A Weakly Supervised Consistency-based Learning Method for COVID-19 Segmentation in CT Images [link]Paper  doi  bibtex   
@inproceedings{DBLP:conf/wacv/LaradjiRMLLKP0N21,
  author       = {Issam H. Laradji and
                  Pau Rodr{\'{\i}}guez and
                  Oscar Ma{\~{n}}as and
                  Keegan Lensink and
                  Marco Law and
                  Lironne Kurzman and
                  William Parker and
                  David V{\'{a}}zquez and
                  Derek Nowrouzezahrai},
  title        = {A Weakly Supervised Consistency-based Learning Method for {COVID-19}
                  Segmentation in {CT} Images},
  booktitle    = {{IEEE} Winter Conference on Applications of Computer Vision, {WACV}
                  2021, Waikoloa, HI, USA, January 3-8, 2021},
  pages        = {2452--2461},
  publisher    = {{IEEE}},
  year         = {2021},
  url          = {https://doi.org/10.1109/WACV48630.2021.00250},
  doi          = {10.1109/WACV48630.2021.00250},
  timestamp    = {Wed, 07 Dec 2022 00:00:00 +0100},
  biburl       = {https://dblp.org/rec/conf/wacv/LaradjiRMLLKP0N21.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

Downloads: 0