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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

ISBN: 978-989-758-420-6

ISSN: 2184-4984

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.

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Paper citation in several formats:
Xie, Y.; Li, J. and Pei, Y. (2020). Multiple Instance Learning for Detection of Polyps in Computed Tomographic Colonography Images. In Proceedings of the 6th International Conference on Information and Communication Technologies for Ageing Well and e-Health - ICT4AWE, ISBN 978-989-758-420-6; ISSN 2184-4984, pages 236-240. DOI: 10.5220/0009352002360240

@conference{ict4awe20,
author={Yunshen Xie. and Jianqiang Li. and Yan Pei.},
title={Multiple Instance Learning for Detection of Polyps in Computed Tomographic Colonography Images},
booktitle={Proceedings of the 6th International Conference on Information and Communication Technologies for Ageing Well and e-Health - ICT4AWE,},
year={2020},
pages={236-240},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009352002360240},
isbn={978-989-758-420-6},
issn={2184-4984},
}

TY - CONF

JO - Proceedings of the 6th International Conference on Information and Communication Technologies for Ageing Well and e-Health - ICT4AWE,
TI - Multiple Instance Learning for Detection of Polyps in Computed Tomographic Colonography Images
SN - 978-989-758-420-6
IS - 2184-4984
AU - Xie, Y.
AU - Li, J.
AU - Pei, Y.
PY - 2020
SP - 236
EP - 240
DO - 10.5220/0009352002360240

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