Determining the scale of image patches using a deep learning approach

Sebastian Otalora, Oscar Perdomo, Manfredo Atzori, Mats Andersson, Ludwig Jacobsson, Martin Hedlund, Henning Muller

Resultado de la investigación: Capítulo en Libro/Reporte/ConferenciaContribución a la conferencia

4 Citas (Scopus)

Resumen

Detecting the scale of histopathology images is important because it allows to exploit various sources of information to train deep learning (DL) models to recognise biological structures of interest. Large open access databases with images exist, such as The Cancer Genome Atlas (TCGA) and PubMed Central but very few models can use such datasets because of the variability of the data in color and scale and a lack of metadata. In this article, we present and compare two deep learning architectures, to detect the scale of histopathology image patches. The approach is evaluated on a patch dataset from whole slide images of the prostate, obtaining a Cohen's kappa coefficient of 0.9897 in the classification of patches with a scale of 5×, 10× and 20×. The good results represent a first step towards magnification detection in histopathology images that can help to solve the problem on more heterogeneous data sources.

Idioma originalInglés estadounidense
Título de la publicación alojada2018 IEEE 15th International Symposium on Biomedical Imaging, ISBI 2018
EditorialIEEE Computer Society
Páginas843-846
Número de páginas4
ISBN (versión digital)9781538636367
DOI
EstadoPublicada - may 23 2018
Publicado de forma externa
Evento15th IEEE International Symposium on Biomedical Imaging, ISBI 2018 - Washington, Estados Unidos
Duración: abr 4 2018abr 7 2018

Serie de la publicación

NombreProceedings - International Symposium on Biomedical Imaging
Volumen2018-April
ISSN (versión impresa)1945-7928
ISSN (versión digital)1945-8452

Conferencia

Conferencia15th IEEE International Symposium on Biomedical Imaging, ISBI 2018
País/TerritorioEstados Unidos
CiudadWashington
Período4/4/184/7/18

All Science Journal Classification (ASJC) codes

  • Ingeniería biomédica
  • Radiología, medicina nuclear y obtención de imágenes

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