Abstract: Recognition of AML Blast Cells in a Curated Single-Cell Dataset of Leukocyte Morphologies Using Deep Convolutional Neural Networks

  • Christian MatekEmail author
  • Simone Schwarz
  • Karsten Spiekermann
  • Carsten Marr
Conference paper
Part of the Informatik aktuell book series (INFORMAT)


Reliable recognition and microscopic differentiation of malignant and non-malignant leukocytes from peripheral blood smears is a key task of cytological diagnostics in hematology [1]. Having been practised for well over a century, cytomorphological analysis is still today routinely performed by human examiners using optical microscopes, a process that can be tedious, time-consuming, and suffering from considerable intra-and inter-rater variability [2]. Our work aims to provide a more quantitative and robust decision-aid for the differentiation of single blood cells in general and recognition of blast cells characteristic for Acute Myeloid Leukemia (AML) in particular.


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Copyright information

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

Authors and Affiliations

  • Christian Matek
    • 1
    • 2
    Email author
  • Simone Schwarz
    • 2
  • Karsten Spiekermann
    • 2
    • 3
    • 4
  • Carsten Marr
    • 1
  1. 1.Institute of Computational BiologyHelmholtz Zentrum München - German Research Center for Environmental HealthNeuherbergDeutschland
  2. 2.Laboratory of Leukemia Diagnostics, Department of Medicine IIIUniversity Hospital, LMU MunichMunichDeutschland
  3. 3.German Cancer Consortium (DKTK)HeidelbergDeutschland
  4. 4.German Cancer Research Center (DKFZ)HeidelbergDeutschland

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