Efficient annotation bootstrapping for cell identification in follicular lymphoma.

Journal: Computer methods and programs in biomedicine
Published Date:

Abstract

BACKGROUND AND OBJECTIVE: In the medical field of digital pathology, many tasks rely on visual assessments of tissue patterns or cells, presenting an opportunity to apply computer vision methods. However, acquiring a substantial number of annotations for developing deep learning algorithms remains a bottleneck. The annotation process is inherently biased due to various constraints, including labor shortages, high costs, time inefficiencies, and a strongly imbalanced distribution of labels. This study explores available solutions for reducing the costs of annotation bootstrapping in the challenging task of follicular lymphoma diagnosis.

Authors

  • Adam Krawczyk
    IDEAS NCBR, Chmielna 69, Warsaw, 00-801, Poland; Poznan University of Technology, Faculty of Computing and Telecommunications, Piotrowo 2, Poznań, 60-965, Poland. Electronic address: adam.krawczyk@cs.put.poznan.pl.
  • Aleksandra Osowska-Kurczab
    IDEAS NCBR, Chmielna 69, Warsaw, 00-801, Poland. Electronic address: aleksandra.osowska-kurczab@ideas-ncbr.pl.
  • Sławomir Pakuło
    Maria Sklodowska-Curie National Research Institute of Oncology, Tumor Pathology Department, Wybrzeże Armii Krajowej 15, Gliwice, 44-102, Poland.
  • Wojciech Kotłowski
    Poznan University of Technology, Faculty of Computing and Telecommunications, Piotrowo 2, Poznań, 60-965, Poland. Electronic address: wojciech.kotlowski@cs.put.poznan.pl.
  • Zaneta Swiderska-Chadaj
    Department of Pathology, Radboud University Medical Center, The Netherlands. Electronic address: zaneta.swiderska@radboudumc.nl.