title: Exploring Deep Learning and Transfer Learning for Colonic Polyp Classification.
abstract: Recently, Deep Learning, especially through Convolutional Neural Networks (CNNs) has been widely used to enable the extraction of highly representative features. This is done among the network layers by filtering, selecting, and using these features in the last fully connected layers for pattern classification. However, CNN training for automated endoscopic image classification still provides a challenge due to the lack of large and publicly available annotated databases. In this work we explore Deep Learning for the automated classification of colonic polyps using different configurations for training CNNs from scratch (or full training) and distinct architectures of pretrained CNNs tested on 8-HD-endoscopic image databases acquired using different modalities. We compare our results with some commonly used features for colonic polyp classification and the good results suggest that features learned by CNNs trained from scratch and the "off-the-shelf" CNNs features can be highly relevant for automated classification of colonic polyps. Moreover, we also show that the combination of classical features and "off-the-shelf" CNNs features can be a good approach to further improve the results.
authors.affiliation: Department of Computer Sciences, University of Salzburg, Salzburg, Austria; Department of Computer Sciences, Federal University of Tocantins, Palmas, TO, Brazil.
authors.forename: Eduardo
authors.identifier: 0000-0001-9280-4345
authors.initials: E
authors.lastname: Ribeiro
authors.affiliation: Department of Computer Sciences, University of Salzburg, Salzburg, Austria.
authors.forename: Andreas
authors.initials: A
authors.lastname: Uhl
authors.affiliation: Department of Computer Sciences, University of Salzburg, Salzburg, Austria.
authors.forename: Georg
authors.initials: G
authors.lastname: Wimmer
authors.affiliation: St. Elisabeth Hospital, Vienna, Austria.
authors.forename: Michael
authors.initials: M
authors.lastname: Häfner
mesh_terms: D000465:Algorithms; D003111:Colonic Polyps / Q000145:classification / Q000000981:diagnostic imaging*; D003113:Colonoscopy; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004724:Endoscopy; D006801:Humans; D007091:Image Processing, Computer-Assisted; D000069550:Machine Learning; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated; D015203:Reproducibility of Results; D012984:Software; D014741:Video Recording