Utility of unsupervised deep learning using a 3D variational autoencoder in detecting inner ear abnormalities on CT images.

Journal: Computers in biology and medicine
Published Date: Jun 1, 2022

Abstract

BACKGROUND AND PURPOSE: To examine the diagnostic performance of unsupervised deep learning using a 3D variational autoencoder (VAE) for detecting and localizing inner ear abnormalities on CT images.

Authors

  • Masaki Ogawa
    Department of Radiology, Nagoya City University Graduate School of Medical Sciences, Japan. Electronic address: ogawam.med@gmail.com.
  • Masaya Kisohara
    Department of Medical Education, Nagoya City University Graduate School of Medical Sciences, 1 Kawasumi Mizuho-cho Mizuho-ku, Nagoya, 467-8601, Japan.
  • Tatsuhito Yamamoto
    Department of Radiology, Nagoya City University Graduate School of Medical Sciences, Japan.
  • Shunsuke Shibata
    Department of Radiology, Nagoya City University Graduate School of Medical Sciences, Japan.
  • Yoshinao Ojio
    Department of Radiology, Nagoya City University Graduate School of Medical Sciences, Japan.
  • Kanako Mochizuki
    Department of Radiology, Nagoya City University Graduate School of Medical Sciences, Japan.
  • Ayame Tatsuta
    Department of Radiology, Nagoya City University Graduate School of Medical Sciences, Japan.
  • Shinichi Iwasaki
    Department of Otolaryngology & Head and Neck Surgery, Nagoya City University Graduate School of Medical Sciences, Japan.
  • Yuta Shibamoto
    Department of Radiology, Nagoya City University Graduate School of Medical Sciences, Japan.

Keywords

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