Polyp characterization using deep learning and a publicly accessible polyp video database.

Journal: Digestive endoscopy : official journal of the Japan Gastroenterological Endoscopy Society
Published Date: Jan 18, 2023

Abstract

OBJECTIVES: Convolutional neural networks (CNN) for computer-aided diagnosis of polyps are often trained using high-quality still images in a single chromoendoscopy imaging modality with sessile serrated lesions (SSLs) often excluded. This study developed a CNN from videos to classify polyps as adenomatous or nonadenomatous using standard narrow-band imaging (NBI) and NBI-near focus (NBI-NF) and created a publicly accessible polyp video database.

Authors

  • Rawen Kader
    Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London, UK.
  • Antón Cid-Mejías
    Biomedical Informatics Group (GIB), Escuela Técnica Superior de Ingenieros Informáticos, Universidad Politécnica de Madrid, Campus de Montegancedo S/N, Madrid 28660, Spain.
  • Patrick Brandao
    Wellcome/EPSRC Centre for Interventional & Surgical Sciences, University College London, London, UK.
  • Shahraz Islam
    Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London, UK.
  • Sanjith Hebbar
    Odin Vision Ltd, London, UK.
  • Juana González-Bueno Puyal
    Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London, UK.
  • Omer F Ahmad
    Department of Gastroenterology, University College London Hospitals NHS Foundation Trust, 250 Euston Road, London, NW1 2BU, United Kingdom; Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS), University College London, Charles Bell House, 43-45 Foley Street, London, W1W 7TS, United Kingdom. Electronic address: ofahmad123@gmail.com.
  • Mohamed Hussein
    Division of Surgery and Interventional Sciences, University College London, London, UK; Department of Gastroenterology, University College London Hospital, London, UK. Electronic address: mohamed.hussein3@nhs.net.
  • Daniel Toth
    Siemens Healthineers, Frimley, UK. daniel.toth@kcl.ac.uk.
  • Peter Mountney
    Siemens Healthineers, Medical Imaging Technologies, Princeton, NJ, USA.
  • Ed Seward
    Division of Surgery and Interventional Sciences, University College London, London, UK.
  • Roser Vega
    Gastrointestinal Services, University College London Hospital, London, UK.
  • Danail Stoyanov
    University College London, London, UK.
  • Laurence B Lovat
    Division of Surgery & Interventional Science, University College London, London, UK.

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