ub.xmlui.mirage2.page-structure.muninLogoub.xmlui.mirage2.page-structure.openResearchArchiveLogo
    • EnglishEnglish
    • norsknorsk
  • Velg spraaknorsk 
    • EnglishEnglish
    • norsknorsk
  • Administrasjon/UB
Vis innførsel 
  •   Hjem
  • Fakultet for naturvitenskap og teknologi
  • Institutt for informatikk
  • Artikler, rapporter og annet (informatikk)
  • Vis innførsel
  •   Hjem
  • Fakultet for naturvitenskap og teknologi
  • Institutt for informatikk
  • Artikler, rapporter og annet (informatikk)
  • Vis innførsel
JavaScript is disabled for your browser. Some features of this site may not work without it.

Efficient disease detection in gastrointestinal videos – global features versus neural networks

Permanent lenke
https://hdl.handle.net/10037/13321
DOI
https://doi.org/10.1007/s11042-017-4989-y
Thumbnail
Åpne
article.pdf (7.532Mb)
Publisher's version (PDF)
Dato
2017-07-19
Type
Journal article
Tidsskriftartikkel
Peer reviewed

Forfatter
Pogorelov, Konstantin; Riegler, Michael; Eskeland, Sigrun Losada; de Lange, Thomas; Johansen, Dag; Griwodz, Carsten; Schmidt, Peter Thelin; Halvorsen, Pål
Sammendrag
Analysis 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.
Beskrivelse
Source at https://doi.org/10.1007/s11042-017-4989-y.
Forlag
Springer Verlag (Germany)
Sitering
Pogorelov, 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-y
Metadata
Vis full innførsel
Samlinger
  • Artikler, rapporter og annet (informatikk) [478]

Bla

Bla i hele MuninEnheter og samlingerForfatterlisteTittelDatoBla i denne samlingenForfatterlisteTittelDato
Logg inn

Statistikk

Antall visninger
UiT

Munin bygger på DSpace

UiT Norges Arktiske Universitet
Universitetsbiblioteket
uit.no/ub - munin@ub.uit.no

Tilgjengelighetserklæring