Deep learning for the automatic classification of tissue types in breast biopsies
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Deep learning for the automatic classification of tissue types in breast biopsiesData
2019Cita bibliográfica
Córdoba, J., Deniz, O., Bueno, G. (2019). Deep learning for the automatic classification of tissue types in breast biopsies. En XL Jornadas de Automática: libro de actas, Ferrol, 4-6 de septiembre de 2019 (pp. 48-54). DOI capítulo: https://doi.org/10.17979/spudc.9788497497169.048. DOI libro: https://doi.org/10.17979/spudc.9788497497169
Resumo
[Abstract] 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.
Palabras chave
Deep learning
Convolutional neural networks
Breast biopsy
Tissue classification
Convolutional neural networks
Breast biopsy
Tissue classification
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978-84-9749-716-9