Improving machine learning-based bitewing segmentation with synthetic data.

Journal: Journal of dentistry
PMID: 40068717

Abstract

OBJECTIVES: Class imbalance in datasets is one of the challenges of machine learning (ML) in medical image analysis. We employed synthetic data to overcome class imbalance when segmenting bitewing radiographs as an exemplary task for using ML.

Authors

  • Ekaterina Tolstaya
    Department of Conservative Dentistry and Periodontology, LMU University Hospital, LMU Munich, Goethestraße 70, 80 336, Munich, Germany.
  • Antonín Tichý
    Institute of Dental Medicine, First Faculty of Medicine of the Charles University and General University Hospital, Prague, Czech Republic.
  • Sebastian Paris
    Department of Oral Diagnostics, Digital Health and Health Services Research, Charité - Universitätsmedizin Berlin, Germany.
  • Falk Schwendicke
    Department of Operative and Preventive Dentistry, Charité - Universitätsmedizin Berlin, Berlin, Germany. falk.schwendicke@charite.de.

Keywords

External Resources

Popular Topics
Recent Journals