COVID-Net L2C-ULTRA: An Explainable Linear-Convex Ultrasound Augmentation Learning Framework to Improve COVID-19 Assessment and Monitoring. Zeng, E. Z., Ebadi, A., Florea, A., & Wong, A. Sensors, 24(5):1664, 03, 2024.
COVID-Net L2C-ULTRA: An Explainable Linear-Convex Ultrasound Augmentation Learning Framework to Improve COVID-19 Assessment and Monitoring. [link]Link  COVID-Net L2C-ULTRA: An Explainable Linear-Convex Ultrasound Augmentation Learning Framework to Improve COVID-19 Assessment and Monitoring. [link]Paper  bibtex   
@article{journals/sensors/ZengEFW24,
  added-at = {2024-05-04T00:00:00.000+0200},
  author = {Zeng, E. Zhixuan and Ebadi, Ashkan and Florea, Adrian and Wong, Alexander},
  biburl = {https://www.bibsonomy.org/bibtex/21ae51a70cb7dba04309defdcd206179b/dblp},
  ee = {https://doi.org/10.3390/s24051664},
  interhash = {d824c685bf2066a6b1839e9b75eff88a},
  intrahash = {1ae51a70cb7dba04309defdcd206179b},
  journal = {Sensors},
  keywords = {dblp},
  month = {03},
  number = 5,
  pages = 1664,
  timestamp = {2024-05-06T07:12:43.000+0200},
  title = {COVID-Net L2C-ULTRA: An Explainable Linear-Convex Ultrasound Augmentation Learning Framework to Improve COVID-19 Assessment and Monitoring.},
  url = {http://dblp.uni-trier.de/db/journals/sensors/sensors24.html#ZengEFW24},
  volume = 24,
  year = 2024
}

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