Light In The Black: An Evaluation of Data Augmentation Techniques for COVID-19 CT’s Semantic Segmentation

  • Bruno A. Krinski UFPR
  • Daniel V. Ruiz UFPR
  • Eduardo Todt UFPR

Resumo


Com a COVID-19, diagnósticos de imagens médicas assistidos por computador ganharam muita atenção, e métodos robustos de Segmentação Semântica de Tomografia Computadorizada (TC) tornaram-se altamente desejáveis. A Segmentação Semântica de TC é um dos muitos campos de pesquisa de detecção automática da COVID-19 e foi amplamente explorado desde o surto da COVID-19. Neste trabalho, propomos uma análise extensiva sobre o quanto diferentes técnicas de aumento de dados contribuem para melhorar o treinamento de redes neurais codificador-decodificador sobre este problema. Vinte técnicas diferentes de aumento de dados foram avaliadas em cinco conjuntos de dados diferentes. Cada conjunto de dados foi validado através de uma estratégia de validação cruzada de cinco subconjuntos, resultando assim em mais de 3.000 experimentos. Nossas descobertas mostram que as transformações de nível espacial são as mais promissoras para melhorar o aprendizado das redes neurais sobre este problema.

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Publicado
07/06/2022
KRINSKI, Bruno A.; RUIZ, Daniel V.; TODT, Eduardo. Light In The Black: An Evaluation of Data Augmentation Techniques for COVID-19 CT’s Semantic Segmentation. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO APLICADA À SAÚDE (SBCAS), 22. , 2022, Teresina. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2022 . p. 156-167. ISSN 2763-8952. DOI: https://doi.org/10.5753/sbcas.2022.222495.

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