Analysis of automatically generated embedding guides for cell classification

International Conference on Image Processing Theory, Tools and Applications (IPTA), vol. 34, pp. 1–6

Abstract

Automated cell classification in human bone marrow microscopy images could lead to faster acquisition and, therefore, to a considerably larger number of cells for the statistical cell count analysis. As basis for the diagnosis of hematopoietic dis-eases such as leukemia, this would be a significant improvement of clinical workflows. The classification of such cells, however, is challenging, partially due to dependencies between different cell types. In 2021, guided representation learning was introduced as an approach to include this domain knowledge into training by providing “embedding guides” as an optimization target for individual cell types. In this work, we propose improvements to guided repre-sentation learning by automatically generating guides based on graph optimization algorithms. We incorporate information about the visual similarity and the impact on diagnosis of mis-classifications. We show that this reduces critical false predictions and improves the overall classification F-score by up to 2.5 percentage points.

Authors 7

1. Philipp Gräbel Aachen Institute of Imaging & Computer Vision

   RWTH Aachen University

   Affiliation as printed

   Institute of Imaging and Computer Vision, RWTH Aachen University,Aachen,Germany

   Institute of Imaging and Computer Vision, RWTH Aachen University, Aachen, Germany

2. Julian Thull Aachen Institute of Imaging & Computer Vision

   RWTH Aachen University

   Affiliation as printed

   Institute of Imaging and Computer Vision, RWTH Aachen University,Aachen,Germany

   Institute of Imaging and Computer Vision, RWTH Aachen University, Aachen, Germany

3. Martina Margrit Crysandt Aachen Department of Hematology, Oncology

   Universitätsklinikum Aachen · RWTH Aachen University

   Affiliation as printed

   University Hospital, RWTH Aachen University,Department of Hematology, Oncology, Hemostaseology and Stem Cell Transplantation,Aachen,Germany

   Department of Hematology, Oncology, Hemostaseology and Stem Cell Transplantation, University Hospital, RWTH Aachen University, Aachen, Germany

4. Barbara M. Klinkhammer Aachen Institute of Pathology

   Universitätsklinikum Aachen · RWTH Aachen University

   Affiliation as printed

   Institute of Pathology, University Hospital, RWTH Aachen University,Aachen,Germany

   Institute of Pathology, University Hospital, RWTH Aachen University, Aachen, Germany

5. Peter Boor Aachen Institute of Pathology

   Universitätsklinikum Aachen · RWTH Aachen University

   Affiliation as printed

   Institute of Pathology, University Hospital, RWTH Aachen University,Aachen,Germany

   Institute of Pathology, University Hospital, RWTH Aachen University, Aachen, Germany

6. Tim H. Brümmendorf Aachen Department of Hematology, Oncology

   Universitätsklinikum Aachen · RWTH Aachen University

   Affiliation as printed

   University Hospital, RWTH Aachen University,Department of Hematology, Oncology, Hemostaseology and Stem Cell Transplantation,Aachen,Germany

   Department of Hematology, Oncology, Hemostaseology and Stem Cell Transplantation, University Hospital, RWTH Aachen University, Aachen, Germany

7. Dorit Merhof Aachen Institute of Imaging & Computer Vision

   RWTH Aachen University

   Affiliation as printed

   Institute of Imaging and Computer Vision, RWTH Aachen University,Aachen,Germany

   Institute of Imaging and Computer Vision, RWTH Aachen University, Aachen, Germany