title: Visual pattern mining in histology image collections using bag of features.
abstract: OBJECTIVE
  The paper addresses the problem of finding visual patterns in histology image collections. In particular, it proposes a method for correlating basic visual patterns with high-level concepts combining an appropriate image collection representation with state-of-the-art machine learning techniques.
  
  
  METHODS
  The proposed method starts by representing the visual content of the collection using a bag-of-features strategy. Then, two main visual mining tasks are performed: finding associations between visual-patterns and high-level concepts, and performing automatic image annotation. Associations are found using minimum-redundancy-maximum-relevance feature selection and co-clustering analysis. Annotation is done by applying a support-vector-machine classifier. Additionally, the proposed method includes an interpretation mechanism that associates concept annotations with corresponding image regions. The method was evaluated in two data sets: one comprising histology images from the different four fundamental tissues, and the other composed of histopathology images used for cancer diagnosis. Different visual-word representations and codebook sizes were tested. The performance in both concept association and image annotation tasks was qualitatively and quantitatively evaluated.
  
  
  RESULTS
  The results show that the method is able to find highly discriminative visual features and to associate them to high-level concepts. In the annotation task the method showed a competitive performance: an increase of 21% in f-measure with respect to the baseline in the histopathology data set, and an increase of 47% in the histology data set.
  
  
  CONCLUSIONS
  The experimental evidence suggests that the bag-of-features representation is a good alternative to represent visual content in histology images. The proposed method exploits this representation to perform visual pattern mining from a wider perspective where the focus is the image collection as a whole, rather than individual images.
authors.affiliation: Bioingenium Research Group, Computer Systems and Industrial Engineering Department, National University of Colombia, Cra 30 No 45 03-Ciudad Universitaria, Faculty of Engineering, Building 453 Office 114, Bogotá DC, Colombia. aacruzr@unal.edu.co
authors.forename: Angel
authors.initials: A
authors.lastname: Cruz-Roa
authors.forename: Juan C
authors.initials: JC
authors.lastname: Caicedo
authors.forename: Fabio A
authors.initials: FA
authors.lastname: González
mesh_terms: D001185:Artificial Intelligence; D057225:Data Mining / Q000379:methods*; D016208:Databases, Factual; D003952:Diagnostic Imaging / Q000379:methods*; D006652:Histological Techniques; D006801:Humans; D009369:Neoplasms / Q000175:diagnosis*; D010363:Pattern Recognition, Automated / Q000379:methods