Corpus ID: 221971047

Quantifying the effect of image compression on supervised learning applications in optical microscopy

@article{Pomarico2020QuantifyingTE,
  title={Quantifying the effect of image compression on supervised learning applications in optical microscopy},
  author={Enrico Pomarico and C'edric Schmidt and Florian Chays and David Nguyen and Arielle L Planchette and Audrey Tissot and Adrien Roux and St{\'e}phane Pag{\`e}s and Laura Batti and Christoph Clausen and Theo Lasser and Aleksandra Radenovi{\'c} and Bruno Sanguinetti and J{\'e}r{\^o}me Extermann},
  journal={ArXiv},
  year={2020},
  volume={abs/2009.12570}
}
The impressive growth of data throughput in optical microscopy has triggered a widespread use of supervised learning (SL) models running on compressed image datasets for efficient automated analysis. However, since lossy image compression risks to produce unpredictable artifacts, quantifying the effect of data compression on SL applications is of pivotal importance to assess their reliability, especially for clinical use. We propose an experimental method to evaluate the tolerability of image… Expand

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