Self-supervised learning for classifying paranasal anomalies in the maxillary sinus.

Journal: International journal of computer assisted radiology and surgery
Published Date: Jun 8, 2024

Abstract

PURPOSE: Paranasal anomalies, frequently identified in routine radiological screenings, exhibit diverse morphological characteristics. Due to the diversity of anomalies, supervised learning methods require large labelled dataset exhibiting diverse anomaly morphology. Self-supervised learning (SSL) can be used to learn representations from unlabelled data. However, there are no SSL methods designed for the downstream task of classifying paranasal anomalies in the maxillary sinus (MS).

Authors

  • Debayan Bhattacharya
    Institute of Medical Technology and Intelligent Systems, Hamburg University of Technology, 21073, Hamburg, Germany.
  • Finn Behrendt
    Institute of Medical Technology and Intelligent Systems, Hamburg University of Technology, Hamburg, Germany.
  • Benjamin Tobias Becker
    Department of Otorhinolaryngology, Head and Neck Surgery and Oncology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
  • Lennart Maack
    Institute of Medical Technology and Intelligent Systems, Technische Universitaet Hamburg, Hamburg, Germany.
  • Dirk Beyersdorff
    Clinic and Polyclinic for Diagnostic and Interventional Radiology and Nuclear Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
  • Elina Petersen
    Population Health Research Department, University Heart and Vascular Center, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
  • Marvin Petersen
    Clinic and Polyclinic for Neurology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
  • Bastian Cheng
    Clinic and Polyclinic for Neurology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
  • Dennis Eggert
    Clinic and Polyclinic for Otolaryngology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
  • Christian Betz
    Clinic and Polyclinic for Otolaryngology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
  • Anna Sophie Hoffmann
    Department of Otorhinolaryngology, Head and Neck Surgery and Oncology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
  • Alexander Schlaefer
    Institute of Medical Technology, Hamburg University of Technology, Hamburg, Germany. schlaefer@tuhh.de.

Keywords

External Resources

Popular Topics
Recent Journals