research product . Lecture . 2021

Leveraging state-of-the-art architectures by enriching training information - a case study

Sölter, Jan; Proverbio, Daniele; Baniasadi, Mehri; Bossa, Matias Nicolas; Vlasov, Vanja; Garcia Santa Cruz, Beatriz; Husch, Andreas;
Open Access English
  • Published: 11 Jan 2021
  • Country: Luxembourg
Our working hypothesis is that key factors in COVID-19 imaging are the available imaging data and their label noise and confounders, rather than network architectures per se. Thus, we applied existing state-of-the-art convolution neural network frameworks based on the U-Net architecture, namely nnU-Net [3], and focused on leveraging the available training data. We did not apply any pre-training nor modi ed the network architecture. First, we enriched training information by generating two additional labels for lung and body area. Lung labels were created with a public available lung segmentation network and weak body labels were generated by thresholding. Subseq...
free text keywords: : Multidisciplinary, general & others [C99] [Engineering, computing & technology], : Multidisciplinaire, généralités & autres [C99] [Ingénierie, informatique & technologie]
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