Transferable automatic hematological cell classification: Overcoming data limitations with self-supervised learning.

Journal: Computer methods and programs in biomedicine
PMID: 39693791

Abstract

BACKGROUND AND OBJECTIVE: Classification of peripheral blood and bone marrow cells is critical in the diagnosis and monitoring of hematological disorders. The development of robust and reliable automatic classification systems is hampered by data scarcity and limited model generalizability across laboratories. The present study proposes the integration of self-supervised learning (SSL) into cell classification pipelines to address these challenges.

Authors

  • Laura Wenderoth
    Institute for Applied Medical Informatics, University Medical Center Hamburg-Eppendorf, Christoph-Probst-Weg 1, 20251 Hamburg, Germany; Institute of Computational Neuroscience, University Medical Center Hamburg-Eppendorf, Martinistr. 52, 20246 Hamburg, Germany; Center for Biomedical Artificial Intelligence (bAIome), University Medical Center Hamburg-Eppendorf, Martinistr. 52, 20246 Hamburg, Germany.
  • Anne-Marie Asemissen
    II. Department of Medicine, University Medical Center Hamburg-Eppendorf, Martinistr. 52, 20246 Hamburg, Germany.
  • Franziska Modemann
    II. Department of Medicine, University Medical Center Hamburg-Eppendorf, Martinistr. 52, 20246 Hamburg, Germany.
  • Maximilian Nielsen
    Department of Computational Neuroscience (M.N., T.S., R.W.), University Medical Center-Hamburg-Eppendorf, Germany.
  • Rene Werner

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