A subset-search and ranking based feature-selection for histology image classification using global and local quantification
J. Coatelen, Adélaïde Albouy-Kissi, Benjamin Albouy-Kissi, Jean-Philippe Coton, Laurence Maunier-Sifre, Juliette Joubert‐Zakeyh, Pierre Déchelotte, Armand Abergel · 2015
Biopsy remains the gold standard for the diagnosis of chronic liver diseases. However, the variability in the diagnostic between readers leads to define a method to objectively describe histologic tissue. A complete framework has been implemented to analyze images of any tissue. Based on subset selection and feature ranking approaches, a feature selection computes the most relevant subset of descriptors in terms of classification from a wide initial list of descriptors. In comparison with equivalent methods, this implementation can find lists of descriptors which are significantly shorter for an equivalent accuracy. Furthermore, it enables the classification of slides using combinations of global and local measurements. The results have pointed that it could reach an accuracy of 90.5% (ROC-AUC=81.1%) in a human liver fibrosis grading approach by selecting 3 of the 457 global and local descriptors. The feature ranking approach gave less accurate subsets than the subset selection.