--- res: bibo_abstract: - Aiming at the automatic diagnosis of tumors from narrow band imaging (NBI) magnifying endoscopy (ME) images of the stomach, we combine methods from image processing, computational topology, and machine learning to classify patterns into normal, tubular, vessel. Training the algorithm on a small number of images of each type, we achieve a high rate of correct classifications. The analysis of the learning algorithm reveals that a handful of geometric and topological features are responsible for the overwhelming majority of decisions.@eng bibo_authorlist: - foaf_Person: foaf_givenName: Olga foaf_name: Dunaeva, Olga foaf_surname: Dunaeva - foaf_Person: foaf_givenName: Herbert foaf_name: Edelsbrunner, Herbert foaf_surname: Edelsbrunner foaf_workInfoHomepage: http://www.librecat.org/personId=3FB178DA-F248-11E8-B48F-1D18A9856A87 orcid: 0000-0002-9823-6833 - foaf_Person: foaf_givenName: Anton foaf_name: Lukyanov, Anton foaf_surname: Lukyanov - foaf_Person: foaf_givenName: Michael foaf_name: Machin, Michael foaf_surname: Machin - foaf_Person: foaf_givenName: Daria foaf_name: Malkova, Daria foaf_surname: Malkova bibo_doi: 10.1109/SYNASC.2014.81 dct_date: 2015^xs_gYear dct_language: eng dct_publisher: IEEE@ dct_title: The classification of endoscopy images with persistent homology@ ...