Exploring Deep Learning and Transfer Learning for Colonic Polyp Classification
Joint Authors
Ribeiro, Eduardo
Uhl, Andreas
Wimmer, Georg
Häfner, Michael
Source
Computational and Mathematical Methods in Medicine
Issue
Vol. 2016, Issue 2016 (31 Dec. 2016), pp.1-16, 16 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-10-26
Country of Publication
Egypt
No. of Pages
16
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Ribeiro, Eduardo& Uhl, Andreas& Wimmer, Georg& Häfner, Michael. 2016. Exploring Deep Learning and Transfer Learning for Colonic Polyp Classification. Computational and Mathematical Methods in Medicine،Vol. 2016, no. 2016, pp.1-16.
https://search.emarefa.net/detail/BIM-1100171
Modern Language Association (MLA)
Ribeiro, Eduardo…[et al.]. Exploring Deep Learning and Transfer Learning for Colonic Polyp Classification. Computational and Mathematical Methods in Medicine No. 2016 (2016), pp.1-16.
https://search.emarefa.net/detail/BIM-1100171
American Medical Association (AMA)
Ribeiro, Eduardo& Uhl, Andreas& Wimmer, Georg& Häfner, Michael. Exploring Deep Learning and Transfer Learning for Colonic Polyp Classification. Computational and Mathematical Methods in Medicine. 2016. Vol. 2016, no. 2016, pp.1-16.
https://search.emarefa.net/detail/BIM-1100171
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1100171