A deep learning system to diagnose the malignant potential of urothelial carcinoma cells in cytology specimens.

Journal: Cancer cytopathology
PMID: 33979039

Abstract

BACKGROUND: Although deep learning algorithms for clinical cytology have recently been developed, their application to practical assistance systems has not been achieved. In addition, whether deep learning systems (DLSs) can perform diagnoses that cannot be performed by pathologists has not been fully evaluated.

Authors

  • Satoshi Nojima
    Department of Pathology, Osaka University Graduate School of Medicine, Osaka, Japan.
  • Kei Terayama
    Graduate School of Medical Life Science, Yokohama City University, Yokohama, Kanagawa 230-0045, Japan.
  • Saeko Shimoura
    Graduate School of Medicine, Kyoto University, Kyoto, Japan.
  • Sachiko Hijiki
    Department of Pathology, Osaka University Graduate School of Medicine, Osaka, Japan.
  • Norio Nonomura
    Department of Urology, Osaka University Graduate School of Medicine, Osaka, Japan.
  • Eiichi Morii
    Department of Pathology, Osaka University Graduate School of Medicine, Suita-city, Osaka.
  • Yasushi Okuno
    Graduate School of Medicine, Kyoto University, Shogoin-kawaharacho, city/>Sakyo-ku Kyoto, 606-8507, Japan.
  • Kazutoshi Fujita
    Department of Urology, Osaka University Graduate School of Medicine, Osaka, Japan.

Keywords

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