Leveraging Explainability methods in Spectral Domain for Data Augmentation and efficient training of CNN classifiers for Covid-19 Detection

Meaza Eyakem Gebreamlak, Meghna P. Ayyar, Jenny Benois-Pineau, Jean-Pierre Salmon, Akka Zemmari. Leveraging Explainability methods in Spectral Domain for Data Augmentation and efficient training of CNN classifiers for Covid-19 Detection. In Twelfth International Conference on Image Processing Theory, Tools and Applications, IPTA 2023, Paris, France, October 16-19, 2023. pages 1-6, IEEE, 2023. [doi]

@inproceedings{GebreamlakABSZ23,
  title = {Leveraging Explainability methods in Spectral Domain for Data Augmentation and efficient training of CNN classifiers for Covid-19 Detection},
  author = {Meaza Eyakem Gebreamlak and Meghna P. Ayyar and Jenny Benois-Pineau and Jean-Pierre Salmon and Akka Zemmari},
  year = {2023},
  doi = {10.1109/IPTA59101.2023.10320061},
  url = {https://doi.org/10.1109/IPTA59101.2023.10320061},
  researchr = {https://researchr.org/publication/GebreamlakABSZ23},
  cites = {0},
  citedby = {0},
  pages = {1-6},
  booktitle = {Twelfth International Conference on Image Processing Theory, Tools and Applications, IPTA 2023, Paris, France, October 16-19, 2023},
  publisher = {IEEE},
  isbn = {979-8-3503-2541-6},
}