Paper Info
Title
Computer assisted optical biopsy for colorectal polyps.
Abstract
We propose a method for computer-assisted optical biopsy for colorectal polyps, with the final goal of assisting the medical expert during the colonoscopy. In particular, we target the problem of automatic classification of polyp images in two classes: adenomatous vs non-adenoma. Our approach is based on recent advancements in convolutional neural networks (CNN) for image representation. In the paper, we describe and compare four different methodologies address the binary classification task: a baseline with classical features and a Random Forest classifier, two methods based on features obtained from a pre-trained network, and finally, the end-to-end training of a CNN. With the pre-trained network, we show the feasibility of transferring a feature extraction mechanism trained on millions of natural images, to the task of classifying adenomatous polyps. We then demonstrate further performance improvements when training the CNN for our specific classification task. In our study, 776 polyp images were acquired and histologically analyzed after polyp resection. We report a performance increase of the CNN-based approaches with respect to both, the conventional engineered features and to a state-of-the-art method based on videos and 3D shape features.
Year
DOI
Venue
2017
10.1117/12.2254595
Proceedings of SPIE
Keywords
Field
DocType
Polyp classification,Colonoscopy,Computer-Aided Diagnosis,Convolutional Neural Networks
Optical Biopsy,Computer vision,Colonoscopy,Binary classification,Convolutional neural network,Computer science,Computer-aided diagnosis,Feature extraction,Artificial intelligence,Artificial neural network,Random forest
Conference
Volume
ISSN
Citations 
10134
0277-786X
0
PageRank 
References 
Authors
0.34
4
7
Name
Order
Citations
PageRank
Fernando J. Navarro-Avila100.34
Yadira Saint-Hill-Febles200.34
Janis Renner300.34
Peter Klare400.34
Stefan von Delius500.34
Nassir Navab66594578.60
Diana Mateus741732.74