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Deep Learning Assisted Diagnosis of Onychomycosis on Whole-Slide Images.

Philipp JansenAdelaida CreosteanuViktor MatyasAmrei DillingAna PinaAndrea SagginiTobias SchimmingJennifer LandsbergBirte BurgdorfSylvia GiaquintaHansgeorg MüllerMichael EmbergerChristian RoseLutz SchmitzCyrill GeraudDirk SchadendorfJörg SchallerMaximilian AlberFrederick KlauschenKlaus Georg Griewank
Published in: Journal of fungi (Basel, Switzerland) (2022)
Our results demonstrate that machine-learning-based algorithms applied to real-world clinical cases can produce comparable sensitivities to human pathologists. Our established U-NET may be used as a supportive diagnostic tool to preselect possible slides with fungal elements. Slides where fungal elements are indicated by our U-NET should be reevaluated by the pathologist to confirm or refute the diagnosis of onychomycosis.
Keyphrases
  • deep learning
  • machine learning
  • artificial intelligence
  • convolutional neural network
  • endothelial cells
  • big data
  • induced pluripotent stem cells
  • pluripotent stem cells