dc.contributor.author | Jha, Debesh | |
dc.contributor.author | Smedsrud, Pia | |
dc.contributor.author | Riegler, Michael | |
dc.contributor.author | Johansen, Dag | |
dc.contributor.author | de Lange, Thomas | |
dc.contributor.author | Halvorsen, Pål | |
dc.contributor.author | Johansen, Håvard D. | |
dc.date.accessioned | 2020-03-04T14:47:59Z | |
dc.date.accessioned | 2020-04-27T10:01:37Z | |
dc.date.available | 2020-03-04T14:47:59Z | |
dc.date.available | 2020-04-27T10:01:37Z | |
dc.date.issued | 2020-01-16 | |
dc.identifier.citation | Jha D, Smedsrud P, Riegler M, Johansen D, de Lange T, Halvorsen P, Johansen HJ: ResUNet++: An Advanced Architecture for Medical Image Segmentation. In: Ceballos. Proceedings of the 2019 IEEE International Symposium on Multimedia ISM 2019, 2019. IEEE p. 225-230 | en |
dc.identifier.isbn | 978-1-7281-5606-4 | |
dc.identifier.uri | https://hdl.handle.net/10642/8489 | |
dc.description.abstract | Accurate computer-aided polyp detection and segmentation during colonoscopy examinations can help endoscopists resect abnormal tissue and thereby decrease chances of polyps growing into cancer. Towards developing a fully automated model for pixel-wise polyp segmentation, we propose ResUNet++, which is an improved ResUNet architecture for colonoscopic image segmentation. Our experimental evaluations show that the suggested architecture produces good segmentation results on publicly available datasets. Furthermore, ResUNet++ significantly outperforms U-Net and ResUNet, two key state-of-the-art deep learning architectures, by achieving high evaluation scores with a dice coefficient of 81.33%, and a mean Intersection over Union (mIoU) of 79.27% for the Kvasir-SEG dataset and a dice coefficient of 79.55%, and a mIoU of 79.62% with CVC-612 dataset. | en |
dc.description.sponsorship | This work is funded in part by Research Council of Norway project number 263248. The computations in this paper were performed on equipment provided by the Experimental Infrastructure for Exploration of Exascale Computing (eX3), which is financially supported by the Research Council of Norway under contract 270053. | en |
dc.language.iso | en | en |
dc.publisher | IEEE | en |
dc.relation.ispartof | 2019 IEEE International Symposium on Multimedia (ISM) | |
dc.relation.ispartofseries | IEEE International Symposium on Multimedia; 2019 IEEE International Symposium on Multimedia (ISM) | |
dc.rights | © 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. DOI: https://dx.doi.org/10.1109/ISM46123.2019.00049 | en |
dc.subject | Cancer | en |
dc.subject | Endoscopes | en |
dc.subject | Image segmentations | en |
dc.subject | Learning | en |
dc.subject | Artificial intelligence | en |
dc.subject | Medical image processings | en |
dc.title | ResUNet++: An Advanced Architecture for Medical Image Segmentation | en |
dc.type | Conference object | en |
dc.date.updated | 2020-03-04T14:47:58Z | |
dc.description.version | acceptedVersion | en |
dc.identifier.doi | https://dx.doi.org/10.1109/ISM46123.2019.00049 | |
dc.identifier.cristin | 1776899 | |
dc.subject.nsi | VDP::Teknologi: 500::Informasjons- og kommunikasjonsteknologi: 550::Datateknologi: 551 | |
dc.subject.nsi | VDP::Technology: 500::Information and communication technology: 550::Computer technology: 551 | |
dc.subject.nsi | VDP::Medisinske fag: 700::Klinisk medisinske fag: 750::Gasteroenterologi: 773 | |
dc.subject.nsi | VDP::Midical sciences: 700::Clinical medical sciences: 750::Gastroenterology: 773 | |
dc.subject.nsi | VDP::Matematikk og naturvitenskap: 400::Informasjons- og kommunikasjonsvitenskap: 420::Simulering, visualisering, signalbehandling, bildeanalyse: 429 | |
dc.subject.nsi | VDP::Mathematics and natural scienses: 400::Information and communication science: 420::Simulation, visualisation, signal processing, image analysis: 429 | |
dc.relation.projectID | Norges forskningsråd: 263248 | |
dc.relation.projectID | Norges forskningsråd: 270053 | |
dc.source.isbn | 978-1-7281-5606-4 | |