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- Publisher Website: 10.1109/TMI.2017.2664042
- Scopus: eid_2-s2.0-85021449496
- PMID: 28182555
- WOS: WOS:000402722500003
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Article: Comparative Validation of Polyp Detection Methods in Video Colonoscopy: Results from the MICCAI 2015 Endoscopic Vision Challenge
Title | Comparative Validation of Polyp Detection Methods in Video Colonoscopy: Results from the MICCAI 2015 Endoscopic Vision Challenge |
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Authors | Bernal, JorgeTajkbaksh, NimaSanchez, Francisco JavierMatuszewski, Bogdan J.Chen, HaoYu, LequanAngermann, QuentinRomain, OlivierRustad, BjornBalasingham, IlangkoPogorelov, KonstantinChoi, SungbinDebard, QuentinMaier-Hein, LenaSpeidel, StefanieStoyanov, DanailBrandao, PatrickCordova, HenrySanchez-Montes, CristinaGurudu, Suryakanth R.Fernandez-Esparrach, GloriaDray, XavierLiang, JianmingHistace, Aymeric |
Keywords | validation framework machine learning polyp detection Endoscopic vision handcrafted features |
Issue Date | 2017 |
Citation | IEEE Transactions on Medical Imaging, 2017, v. 36, n. 6, p. 1231-1249 How to Cite? |
Abstract | Colonoscopy is the gold standard for colon cancer screening though some polyps are still missed, thus preventing early disease detection and treatment. Several computational systems have been proposed to assist polyp detection during colonoscopy but so far without consistent evaluation. The lack of publicly available annotated databases has made it difficult to compare methods and to assess if they achieve performance levels acceptable for clinical use. The Automatic Polyp Detection sub-challenge, conducted as part of the Endoscopic Vision Challenge (http://endovis.grand-challenge.org) at the international conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) in 2015, was an effort to address this need. In this paper, we report the results of this comparative evaluation of polyp detection methods, as well as describe additional experiments to further explore differences between methods. We define performance metrics and provide evaluation databases that allow comparison of multiple methodologies. Results show that convolutional neural networks are the state of the art. Nevertheless, it is also demonstrated that combining different methodologies can lead to an improved overall performance. |
Persistent Identifier | http://hdl.handle.net/10722/299550 |
ISSN | 2021 Impact Factor: 11.037 2020 SCImago Journal Rankings: 2.322 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Bernal, Jorge | - |
dc.contributor.author | Tajkbaksh, Nima | - |
dc.contributor.author | Sanchez, Francisco Javier | - |
dc.contributor.author | Matuszewski, Bogdan J. | - |
dc.contributor.author | Chen, Hao | - |
dc.contributor.author | Yu, Lequan | - |
dc.contributor.author | Angermann, Quentin | - |
dc.contributor.author | Romain, Olivier | - |
dc.contributor.author | Rustad, Bjorn | - |
dc.contributor.author | Balasingham, Ilangko | - |
dc.contributor.author | Pogorelov, Konstantin | - |
dc.contributor.author | Choi, Sungbin | - |
dc.contributor.author | Debard, Quentin | - |
dc.contributor.author | Maier-Hein, Lena | - |
dc.contributor.author | Speidel, Stefanie | - |
dc.contributor.author | Stoyanov, Danail | - |
dc.contributor.author | Brandao, Patrick | - |
dc.contributor.author | Cordova, Henry | - |
dc.contributor.author | Sanchez-Montes, Cristina | - |
dc.contributor.author | Gurudu, Suryakanth R. | - |
dc.contributor.author | Fernandez-Esparrach, Gloria | - |
dc.contributor.author | Dray, Xavier | - |
dc.contributor.author | Liang, Jianming | - |
dc.contributor.author | Histace, Aymeric | - |
dc.date.accessioned | 2021-05-21T03:34:39Z | - |
dc.date.available | 2021-05-21T03:34:39Z | - |
dc.date.issued | 2017 | - |
dc.identifier.citation | IEEE Transactions on Medical Imaging, 2017, v. 36, n. 6, p. 1231-1249 | - |
dc.identifier.issn | 0278-0062 | - |
dc.identifier.uri | http://hdl.handle.net/10722/299550 | - |
dc.description.abstract | Colonoscopy is the gold standard for colon cancer screening though some polyps are still missed, thus preventing early disease detection and treatment. Several computational systems have been proposed to assist polyp detection during colonoscopy but so far without consistent evaluation. The lack of publicly available annotated databases has made it difficult to compare methods and to assess if they achieve performance levels acceptable for clinical use. The Automatic Polyp Detection sub-challenge, conducted as part of the Endoscopic Vision Challenge (http://endovis.grand-challenge.org) at the international conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) in 2015, was an effort to address this need. In this paper, we report the results of this comparative evaluation of polyp detection methods, as well as describe additional experiments to further explore differences between methods. We define performance metrics and provide evaluation databases that allow comparison of multiple methodologies. Results show that convolutional neural networks are the state of the art. Nevertheless, it is also demonstrated that combining different methodologies can lead to an improved overall performance. | - |
dc.language | eng | - |
dc.relation.ispartof | IEEE Transactions on Medical Imaging | - |
dc.subject | validation framework | - |
dc.subject | machine learning | - |
dc.subject | polyp detection | - |
dc.subject | Endoscopic vision | - |
dc.subject | handcrafted features | - |
dc.title | Comparative Validation of Polyp Detection Methods in Video Colonoscopy: Results from the MICCAI 2015 Endoscopic Vision Challenge | - |
dc.type | Article | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1109/TMI.2017.2664042 | - |
dc.identifier.pmid | 28182555 | - |
dc.identifier.scopus | eid_2-s2.0-85021449496 | - |
dc.identifier.volume | 36 | - |
dc.identifier.issue | 6 | - |
dc.identifier.spage | 1231 | - |
dc.identifier.epage | 1249 | - |
dc.identifier.eissn | 1558-254X | - |
dc.identifier.isi | WOS:000402722500003 | - |