title: An unsupervised feature learning framework for basal cell carcinoma image analysis.
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.
  
  
  METHODS
  This paper presents an integrated unsupervised feature learning (UFL) framework for histopathology image analysis that comprises three main stages: (1) local (patch) representation learning using different strategies (sparse autoencoders, reconstruct independent component analysis and topographic independent component analysis (TICA), (2) global (image) representation learning using a bag-of-features representation or a convolutional neural network, and (3) a visual interpretation layer to highlight the most discriminant regions detected by the model. The integrated unsupervised feature learning framework was exhaustively evaluated in a histopathology image dataset for BCC diagnosis.
  
  
  RESULTS
  The experimental evaluation produced a classification performance of 98.1%, in terms of the area under receiver-operating-characteristic curve, for the proposed framework outperforming by 7% the state-of-the-art discrete cosine transform patch-based representation.
  
  
  CONCLUSIONS
  The proposed UFL-representation-based approach outperforms state-of-the-art methods for BCC detection. Thanks to its visual interpretation layer, the method is able to highlight discriminative tissue regions providing a better diagnosis support. Among the different UFL strategies tested, TICA-learned features exhibited the best performance thanks to its ability to capture low-level invariances, which are inherent to the nature of the problem.
authors.affiliation: 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.
authors.forename: John
authors.initials: J
authors.lastname: Arevalo
authors.affiliation: 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.
authors.forename: Angel
authors.initials: A
authors.lastname: Cruz-Roa
authors.affiliation: 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.
authors.forename: Viviana
authors.initials: V
authors.lastname: Arias
authors.affiliation: 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.
authors.forename: Eduardo
authors.initials: E
authors.lastname: Romero
authors.affiliation: 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.
authors.forename: Fabio A
authors.initials: FA
authors.lastname: González
mesh_terms: D019540:Area Under Curve; D057205:Automation, Laboratory; D001706:Biopsy; D002280:Basal Cell Carcinoma / Q000145:classification / Q000473:pathology*; D020000:Decision Support Systems, Clinical; D003661:Decision Support Techniques; D016002:Discriminant Analysis; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D010338:Pathology, Clinical / Q000379:methods*; D011237:Predictive Value of Tests; D012372:ROC Curve; D015203:Reproducibility of Results; D012878:Skin Neoplasms / Q000145:classification / Q000473:pathology*; D013194:Staining and Labeling; D000069558:Unsupervised Machine Learning
keywords: Basal cell carcinoma; Digital pathology; Representation learning; Unsupervised feature learning