Authors:
Yunshen Xie
1
;
Jianqiang Li
1
and
Yan Pei
2
Affiliations:
1
Faculty of Information, Beijing University of Technology, Beijing, 100124, China
;
2
Computer Science Division, University of Aizu, Aizu-wakamatsu, 965-8580, Japan
Keyword(s):
Machine Learning, Computed Tomographic Colonography, Computer Aid Diagnosis, Polyps, Bioinformatics.
Abstract:
Colorectal cancer(CRC) is a significant health problem in the world, the incidence of CRC can be largely preventable by early detection and removal of the polyps before they turn into the malignant structure. Most existing CAD system for polyps detection rely on fully supervised learning which requires the tedious manual annotation and precise colon segmentation. This paper proposed a method based on multiple instance learning and transfer learning. Our scheme firstly extracts many small patches from CTC images by using threshold segmentation method, then a pre-trained model was applied for feature extracting of instances, next pooling operator was used to aggregating these instance features into a bag, finally, classification result was obtained by a classifier. Our proposed method does not rely on accurate colon segmentation and the result show that it can achieve a high accuracy rate.