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- Publisher Website: 10.1109/JBHI.2016.2637004
- Scopus: eid_2-s2.0-85014899291
- PMID: 28114049
- WOS: WOS:000395538500008
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Article: Integrating Online and Offline Three-Dimensional Deep Learning for Automated Polyp Detection in Colonoscopy Videos
Title | Integrating Online and Offline Three-Dimensional Deep Learning for Automated Polyp Detection in Colonoscopy Videos |
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Authors | |
Keywords | convolutional neural networks (CNNs) colonoscopy video computer-aided diagnosis Automated polyp detection deep learning |
Issue Date | 2017 |
Citation | IEEE Journal of Biomedical and Health Informatics, 2017, v. 21, n. 1, p. 65-75 How to Cite? |
Abstract | Automated polyp detection in colonoscopy videos has been demonstrated to be a promising way for colorectal cancer prevention and diagnosis. Traditional manual screening is time consuming, operator dependent, and error prone; hence, automated detection approach is highly demanded in clinical practice. However, automated polyp detection is very challenging due to high intraclass variations in polyp size, color, shape, and texture, and low interclass variations between polyps and hard mimics. In this paper, we propose a novel offline and online three-dimensional (3-D) deep learning integration framework by leveraging the 3-D fully convolutional network (3D-FCN) to tackle this challenging problem. Compared with the previous methods employing hand-crafted features or 2-D convolutional neural network, the 3D-FCN is capable of learning more representative spatio-temporal features from colonoscopy videos, and hence has more powerful discrimination capability. More importantly, we propose a novel online learning scheme to deal with the problem of limited training data by harnessing the specific information of an input video in the learning process. We integrate offline and online learning to effectively reduce the number of false positives generated by the offline network and further improve the detection performance. Extensive experiments on the dataset of MICCAI 2015 Challenge on Polyp Detection demonstrated the better performance of our method when compared with other competitors. |
Persistent Identifier | http://hdl.handle.net/10722/299544 |
ISSN | 2021 Impact Factor: 7.021 2020 SCImago Journal Rankings: 1.293 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Yu, Lequan | - |
dc.contributor.author | Chen, Hao | - |
dc.contributor.author | Dou, Qi | - |
dc.contributor.author | Qin, Jing | - |
dc.contributor.author | Heng, Pheng Ann | - |
dc.date.accessioned | 2021-05-21T03:34:38Z | - |
dc.date.available | 2021-05-21T03:34:38Z | - |
dc.date.issued | 2017 | - |
dc.identifier.citation | IEEE Journal of Biomedical and Health Informatics, 2017, v. 21, n. 1, p. 65-75 | - |
dc.identifier.issn | 2168-2194 | - |
dc.identifier.uri | http://hdl.handle.net/10722/299544 | - |
dc.description.abstract | Automated polyp detection in colonoscopy videos has been demonstrated to be a promising way for colorectal cancer prevention and diagnosis. Traditional manual screening is time consuming, operator dependent, and error prone; hence, automated detection approach is highly demanded in clinical practice. However, automated polyp detection is very challenging due to high intraclass variations in polyp size, color, shape, and texture, and low interclass variations between polyps and hard mimics. In this paper, we propose a novel offline and online three-dimensional (3-D) deep learning integration framework by leveraging the 3-D fully convolutional network (3D-FCN) to tackle this challenging problem. Compared with the previous methods employing hand-crafted features or 2-D convolutional neural network, the 3D-FCN is capable of learning more representative spatio-temporal features from colonoscopy videos, and hence has more powerful discrimination capability. More importantly, we propose a novel online learning scheme to deal with the problem of limited training data by harnessing the specific information of an input video in the learning process. We integrate offline and online learning to effectively reduce the number of false positives generated by the offline network and further improve the detection performance. Extensive experiments on the dataset of MICCAI 2015 Challenge on Polyp Detection demonstrated the better performance of our method when compared with other competitors. | - |
dc.language | eng | - |
dc.relation.ispartof | IEEE Journal of Biomedical and Health Informatics | - |
dc.subject | convolutional neural networks (CNNs) | - |
dc.subject | colonoscopy video | - |
dc.subject | computer-aided diagnosis | - |
dc.subject | Automated polyp detection | - |
dc.subject | deep learning | - |
dc.title | Integrating Online and Offline Three-Dimensional Deep Learning for Automated Polyp Detection in Colonoscopy Videos | - |
dc.type | Article | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1109/JBHI.2016.2637004 | - |
dc.identifier.pmid | 28114049 | - |
dc.identifier.scopus | eid_2-s2.0-85014899291 | - |
dc.identifier.volume | 21 | - |
dc.identifier.issue | 1 | - |
dc.identifier.spage | 65 | - |
dc.identifier.epage | 75 | - |
dc.identifier.isi | WOS:000395538500008 | - |