Assessment of CNNs, transformers, and hybrid architectures in dental image segmentation.

Journal: Journal of dentistry
PMID: 40064460

Abstract

OBJECTIVES: Convolutional Neural Networks (CNNs) have long dominated image analysis in dentistry, reaching remarkable results in a range of different tasks. However, Transformer-based architectures, originally proposed for Natural Language Processing, are also promising for dental image analysis. The present study aimed to compare CNNs with Transformers for different image analysis tasks in dentistry.

Authors

  • Lisa Schneider
    Department of Simulation and Graphics, Otto von Guericke University Magdeburg, Germany.
  • Aleksander Krasowski
    Department of Oral Diagnostics, Digital Health and Health Services Research, Charité-Universitätsmedizin Berlin, Aßmannshauser Str. 4-6, 14197, Berlin, Germany.
  • Vinay Pitchika
    Clinic for Conservative Dentistry and Periodontology, Ludwig-Maximilians-University Munich, Germany.
  • Lisa Bombeck
    Department of Operative, Preventive and Pediatric Dentistry, Charité - Universitätsmedizin, Berlin, Germany.
  • Falk Schwendicke
    Department of Operative and Preventive Dentistry, Charité - Universitätsmedizin Berlin, Berlin, Germany. falk.schwendicke@charite.de.
  • Martha Büttner
    Charité - Universitätsmedizin Berlin, Berlin, Germany. bdjmanuscripts@nature.com.

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