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Deep learning for the automatic classification of tissue types in breast biopsies

    1. [1] Universidad de Castilla-La Mancha

      Universidad de Castilla-La Mancha

      Ciudad Real, España

  • Localización: XL Jornadas de Automática: libro de actas : Ferrol, 4-6 de septiembre de 2019 / José Luis Calvo-Rolle (ed. lit.), José-Luis Casteleiro-Roca (ed. lit.), Isabel Fernández-Ibáñez (ed. lit.), Óscar Fontenla-Romero (ed. lit.), Esteban Jove (ed. lit.), Alberto J. Leira-Rejas (ed. lit.), José Antonio López Vázquez (ed. lit.), Vanesa Loureiro-Vázquez (ed. lit.), María-Carmen Meizoso-López (ed. lit.), Francisco Javier Pérez Castelo (ed. lit.), Andrés José Piñón Pazos (ed. lit.), Héctor Quintián (ed. lit.), Juan Manuel Rivas Rodríguez (ed. lit.), Benigno Antonio Rodríguez Gómez (ed. lit.), Rafael Alejandro Vega Vega (ed. lit.), 2019, ISBN 978-84-9749-716-9, págs. 48-54
  • Idioma: inglés
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  • Resumen
    • Breast biopsies are crucial in the process of detec ing a wide range of diseases such as breast cancer. The evaluation of these biopsies is performed by trained pathologists that are often overworked due to the increasing number of pathologies requested. Automatic tumour detection techniques have been developed, achieving very good results. In this work, we propose to classify breast biopsies in all the different types of tissue present in them. The tissue types were identified by hand-labeling them following the indications of an expert pathologist. Afterward, they were trained with diffeerent convolutional neural networks such as GoogleNet, AlexNet, SqueezeNet and DenseNet. Out of these four networks, GoogleNet outperformed all of them achieving 95.4% of accuracy. Finally, we tried to identify why the networks were underperforming while also suggesting how results could be improved.


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