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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.date.accessioned2020-01-14T08:43:56Z
dc.date.available2020-01-14T08:43:56Z
dc.date.created2019-08-22T12:58:43Z
dc.date.issued2019
dc.identifier.citationInternational Symposium on Medical Information and Communication Technology. 2019, 2019-May 1-6.nb_NO
dc.identifier.issn2326-828X
dc.identifier.urihttp://hdl.handle.net/11250/2636081
dc.description.abstractAutomatic polyp detection and segmentation are highly desirable for colon screening due to polyp miss rate by physicians during colonoscopy, which is about 25%. However, this computerization is still an unsolved problem due to various polyp-like structures in the colon and high interclass polyp variations in terms of size, color, shape and texture. In this paper, we adapt Mask R-CNN and evaluate its performance with different modern convolutional neural networks (CNN) as its feature extractor for polyp detection and segmentation. We investigate the performance improvement of each feature extractor by adding extra polyp images to the training dataset to answer whether we need deeper and more complex CNNs, or better dataset for training in automatic polyp detection and segmentation. Finally, we propose an ensemble method for further performance improvement. We evaluate the performance on the 2015 MICCAI polyp detection dataset. The best results achieved are 72.59% recall, 80% precision, 70.42% dice, and 61.24% jaccard. The model achieved state-of-the-art segmentation performance.nb_NO
dc.language.isoengnb_NO
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)nb_NO
dc.titlePolyp detection and segmentation using Mask R-CNN: Does a deeper feature extractor CNN always perform better?nb_NO
dc.typeJournal articlenb_NO
dc.typePeer reviewednb_NO
dc.description.versionacceptedVersionnb_NO
dc.source.pagenumber1-6nb_NO
dc.source.volume2019-Maynb_NO
dc.source.journalInternational Symposium on Medical Information and Communication Technologynb_NO
dc.identifier.doi10.1109/ISMICT.2019.8743694
dc.identifier.cristin1717998
dc.description.localcode© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.nb_NO
cristin.unitcode194,63,35,0
cristin.unitnameInstitutt for elektroniske systemer
cristin.ispublishedtrue
cristin.fulltextpostprint
cristin.qualitycode1


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