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dc.contributor.authorPogorelov, Konstantin
dc.contributor.authorRiegler, Michael
dc.contributor.authorEskeland, Sigrun Losada
dc.contributor.authorde Lange, Thomas
dc.contributor.authorJohansen, Dag
dc.contributor.authorGriwodz, Carsten
dc.contributor.authorSchmidt, Peter Thelin
dc.contributor.authorHalvorsen, Pål
dc.date.accessioned2018-08-01T08:05:38Z
dc.date.available2018-08-01T08:05:38Z
dc.date.issued2017-07-19
dc.description.abstractAnalysis of medical videos from the human gastrointestinal (GI) tract for detection and localization of abnormalities like lesions and diseases requires both high precision and recall. Additionally, it is important to support efficient, real-time processing for live feedback during (i) standard colonoscopies and (ii) scalability for massive population-based screening, which we conjecture can be done using a wireless video capsule endoscope (camera-pill). Existing related work in this field does neither provide the necessary combination of accuracy and performance for detecting multiple classes of abnormalities simultaneously nor for particular disease localization tasks. In this paper, a complete end-to-end multimedia system is presented where the aim is to tackle automatic analysis of GI tract videos. The system includes an entire pipeline ranging from data collection, processing and analysis, to visualization. The system combines deep learning neural networks, information retrieval, and analysis of global and local image features in order to implement multi-class classification, detection and localization. Furthermore, it is built in a modular way, so that it can be easily extended to deal with other types of abnormalities. Simultaneously, the system is developed for efficient processing in order to provide real-time feedback to the doctors and for scalability reasons when potentially applied for massive population-based algorithmic screenings in the future. Initial experiments show that our system has multi-class detection accuracy and polyp localization precision at least as good as state-of-the-art systems, and provides additional novelty in terms of real-time performance, low resource consumption and ability to extend with support for new classes of diseases.en_US
dc.descriptionSource at <a href=https://doi.org/10.1007/s11042-017-4989-y> https://doi.org/10.1007/s11042-017-4989-y</a>.en_US
dc.identifier.citationPogorelov, K., Riegler, M., Eskeland, S.L., de Lange, T., Johansen, D., Griwodz, C., ... Halvorsen, P. (2017). Efficient disease detection in gastrointestinal videos – global features versus neural networks. Multimedia tools and applications, 76, 22493-22525. https://doi.org/10.1007/s11042-017-4989-yen_US
dc.identifier.cristinIDFRIDAID 1484110
dc.identifier.doi10.1007/s11042-017-4989-y
dc.identifier.issn1380-7501
dc.identifier.issn1573-7721
dc.identifier.urihttps://hdl.handle.net/10037/13321
dc.language.isoengen_US
dc.publisherSpringer Verlag (Germany)en_US
dc.relation.journalMultimedia tools and applications
dc.relation.projectIDinfo:eu-repo/grantAgreement/RCN/FRINATEK/231687/Norway/Efficient Execution of Large Workloads on Elastic Heterogeneous Resources//en_US
dc.relation.projectIDinfo:eu-repo/grantAgreement/RCN/INTPART/250138/Norway/Trans-Atlantic Corpore Sano//en_US
dc.rights.accessRightsopenAccessen_US
dc.subjectVDP::Teknologi: 500::Medisinsk teknologi: 620en_US
dc.subjectVDP::Technology: 500::Medical technology: 620en_US
dc.subjectMedicalen_US
dc.subjectAutomatic disease detectionen_US
dc.subjectAlgorithmic screeningen_US
dc.subjectGlobal and local image featuresen_US
dc.subjectDeep learning neural networksen_US
dc.subjectInformation retrievalen_US
dc.subjectPerformance evaluationen_US
dc.titleEfficient disease detection in gastrointestinal videos – global features versus neural networksen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US
dc.typePeer revieweden_US


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