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dc.contributor.authorQadir, Hemin Ali Qadir
dc.contributor.authorShin, Younghak
dc.contributor.authorSolhusvik, Johannes
dc.contributor.authorBergsland, Jacob
dc.contributor.authorAabakken, Lars
dc.contributor.authorBalasingham, Ilangko
dc.identifier.citationMedical Image Analysis. 2020, 68 .en_US
dc.description.abstractTo decrease colon polyp miss-rate during colonoscopy, a real-time detection system with high accuracy is needed. Recently, there have been many efforts to develop models for real-time polyp detection, but work is still required to develop real-time detection algorithms with reliable results. We use single-shot feed-forward fully convolutional neural networks (F-CNN) to develop an accurate real-time polyp detection system. F-CNNs are usually trained on binary masks for object segmentation. We propose the use of 2D Gaussian masks instead of binary masks to enable these models to detect different types of polyps more effectively and efficiently and reduce the number of false positives. The experimental results showed that the proposed 2D Gaussian masks are efficient for detection of flat and small polyps with unclear boundaries between background and polyp parts. The masks make a better training effect to discriminate polyps from the polyp-like false positives. The proposed method achieved state-of-the-art results on two polyp datasets. On the ETIS-LARIB dataset we achieved 86.54% recall, 86.12% precision, and 86.33% F1-score, and on the CVC-ColonDB we achieved 91% recall, 88.35% precision, and F1-score 89.65%.en_US
dc.rightsNavngivelse 4.0 Internasjonal*
dc.subjectKreft i tykktarm og endetarmen_US
dc.subjectColorectal canceren_US
dc.titleToward real-time polyp detection using fully CNNs for 2D Gaussian shapes predictionen_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.subject.nsiVDP::Medisinsk teknologi: 620en_US
dc.subject.nsiVDP::Medical technology: 620en_US
dc.source.journalMedical Image Analysisen_US
dc.description.localcode© 2020 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (

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Navngivelse 4.0 Internasjonal
Except where otherwise noted, this item's license is described as Navngivelse 4.0 Internasjonal