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

Debayan BhattacharyaFinn BehrendtBenjamin Tobias BeckerLennart MaackDirk BeyersdorffElina PetersenMarvin PetersenBastian ChengDennis EggertChristian BetzAnna Sophie HoffmannAlexander Schlaefer
Published in: International journal of computer assisted radiology and surgery (2024)
A self-supervised learning approach that inherently focuses on localizing paranasal anomalies proves to be advantageous, particularly when the subsequent task involves differentiating normal from anomalous maxillary sinuses. Access our code at https://github.com/mtec-tuhh/self-supervised-paranasal-anomaly .
Keyphrases
  • machine learning
  • cone beam computed tomography
  • contrast enhanced