An unsupervised feature learning framework for basal cell carcinoma image analysis.

Journal: Artificial intelligence in medicine
Published Date: Apr 23, 2015

Abstract

OBJECTIVE: The paper addresses the problem of automatic detection of basal cell carcinoma (BCC) in histopathology images. In particular, it proposes a framework to both, learn the image representation in an unsupervised way and visualize discriminative features supported by the learned model.

Authors

  • John Arevalo
    Machine Learning, Perception and Discovery Lab, Systems and Computer Engineering Department, Universidad Nacional de Colombia, Faculty of Engineering, Cra 30 No 45 03-Ciudad Universitaria, Building 453 Office 114, Bogotá DC, Colombia. Electronic address: jearevaloo@unal.edu.co.
  • Angel Cruz-Roa
    Machine Learning, Perception and Discovery Lab, Systems and Computer Engineering Department, Universidad Nacional de Colombia, Faculty of Engineering, Cra 30 No 45 03-Ciudad Universitaria, Building 453 Office 114, Bogotá DC, Colombia. Electronic address: aacruzr@unal.edu.co.
  • Viviana Arias
    Pathology Department, Universidad Nacional de Colombia, Faculty of Medicine, Cra 30 No 45 03-Ciudad Universitaria, Bogotá DC, Colombia. Electronic address: vlariasp@unal.edu.co.
  • Eduardo Romero
    Computer Imaging & Medical Applications Laboratory, Universidad Nacional de Colombia, Faculty of Medicine, Cra 30 No 45 03-Ciudad Universitaria, Bogotá DC, Colombia. Electronic address: edromero@unal.edu.co.
  • Fabio A González
    Machine Learning, Perception and Discovery Lab, Systems and Computer Engineering Department, Universidad Nacional de Colombia, Faculty of Engineering, Cra 30 No 45 03-Ciudad Universitaria, Building 453 Office 114, Bogotá DC, Colombia. Electronic address: fagonzalezo@unal.edu.co.

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