Inter-Species, Inter-Tissue Domain Adaptation for Mitotic Figure Assessment

Learning New Tricks from Old Dogs
  • Marc AubrevilleEmail author
  • Christof A. Bertram
  • Samir Jabari
  • Christian Marzahl
  • Robert Klopfleisch
  • Andreas Maier
Conference paper
Part of the Informatik aktuell book series (INFORMAT)


For histopathological tumor assessment, the count of mitotic figures per area is an important part of prognostication. Algorithmic approaches - such as for mitotic figure identification - have significantly improved in recent times, potentially allowing for computer-augmented or fully automatic screening systems in the future. This trend is further supported by whole slide scanning microscopes becoming available in many pathology labs and could soon become a standard imaging tool. For an application in broader fields of such algorithms, the availability of mitotic figure data sets of sufficient size for the respective tissue type and species is an important precondition, that is, however, rarely met. While algorithmic performance climbed steadily for e.g. human mammary carcinoma, thanks to several challenges held in the field, for many tumor types, data sets are not available. In this work, we assess domain transfer of mitotic figure recognition using domain adversarial training on four data sets, two from dogs and two from humans. We were able to show that domain adversarial training considerably improves accuracy when applying mitotic _gure classification learned from the canine on the human data sets (up to +12.8% in accuracy) and is thus a helpful method to transfer knowledge from existing data sets to new tissue types and species.


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Copyright information

© Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature 2020

Authors and Affiliations

  • Marc Aubreville
    • 1
    Email author
  • Christof A. Bertram
    • 2
  • Samir Jabari
    • 3
  • Christian Marzahl
    • 1
  • Robert Klopfleisch
    • 2
  • Andreas Maier
    • 1
  1. 1.Pattern Recognition Lab, Computer SciencesFriedrich-Alexander-Universität Erlangen-NürnbergErlangenDeutschland
  2. 2.Institute of Veterinary PathologyFreie Universität BerlinBerlinDeutschland
  3. 3.Institute of NeuropathologyFriedrich-Alexander-Universität Erlangen-NürnbergErlangenDeutschland

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