Data augmentation study for rare diseases assessment with Deep Learning: Confocal Imaging analysis of Congenital Muscular Dystrophy

  1. Frías, M.
  2. Jiménez Mallebrera, C.
  3. Badosa, C.
  4. Porta, J.M.
  5. Roldán, M.
Libro:
CASEIB 2023. Libro de Actas del XLI Congreso Anual de la Sociedad Española de Ingeniería Biomédica: Contribuyendo a la salud basada en valor
  1. Joaquín Roca González (coord.)
  2. Dolores Ojados González (coord.)
  3. Juan Suardíaz Muro (coord.)

Editorial: Universidad Politécnica de Cartagena

ISBN: 978-84-17853-76-1

Año de publicación: 2023

Páginas: 460-463

Congreso: Congreso Anual de la Sociedad Española de Ingeniería Biomédica. CASEIB (41. 2023. Cartagena)

Tipo: Aportación congreso

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Resumen

Artificial Intelligence (AI) algorithms are widely used in healthcare nowadays. However, there are still fields where the application of these technologies could be challenging, such as rare diseases. In these cases, the main challenge arises from the reduced size of the available data sets. This paper proposes a data augmentation pipeline to address this challenge when using a Deep Learning (DL) algorithm to assess fibroblast cultures from skin biopsies to diagnose Collagen VI-related Congenital Muscular Dystrophy (COL6-CMD). Different data augmentation schemes are described in the literature. However, they must be used cautiously since they might result in overfitting. The results presented in this paper demonstrate that the right combination of data augmentation techniques results in a high diagnostic accuracy (up to 75.35% for the best approach) even with a scarce amount of data.

Fuente de los datos: Dialnet