Classifying Prostate Histological Images Using Deep Gaussian Processes on a New Optical Density Granulometry-Based Descriptor

  • Miguel López-PérezEmail author
  • Adrián Colomer
  • María A. Sales
  • Rafael Molina
  • Valery Naranjo
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11871)


The increasing use of whole slide digital scanners has led to an enormous interest in the application of machine learning techniques to detect prostate cancer using eosin and hematoxylin stained histopathological images. In this work the above problem is approached as follows: the optical density of each whole slide image is calculated and its eosin and hematoxylin concentration components estimated. Then, hand-crafted features, which are expected to capture the expertise of pathologists, are extracted from patches of these two concentration components. Finally, patches are classified using a Deep Gaussian Process on the extracted features. The new approach outperforms current state of the art shallow as well as deep classifiers like InceptionV3, Xception and VGG19 with an AUC value higher than 0.98.


Prostate cancer Optical Density Texture features Morphological features Deep Gaussian Processes 


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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Departamento de Ciencias de la Computación e I.A.University of GranadaGranadaSpain
  2. 2.Instituto de Investigación e Innovación en BioingenieríaI3B, Universitat Politècnica de ValènciaValenciaSpain
  3. 3.Servicio de Anatomía PatológicaHospital Clínico Universitario de ValenciaValenciaSpain

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