Surrounding Cell Suppression For Unsupervised Representation Learning In Hematological Cell Classification

IEEE International Symposium on Biomedical Imaging (ISBI), vol. 8, pp. 526–530

Abstract

Analysis of hematopoietic cells in bone marrow images is a newly emerging field in computer vision. Deep neural networks provide promising approaches for detection and classification tasks in this field. However, labelling a sufficiently large amount of images by medical experts is infeasible in practice. This can be resolved by semi-supervised methods that use image reconstruction as a way to incorporate images without labelled cells. However, this inevitably leads to an inclusion of surrounding cells into the learned representation. We propose and analyze several techniques for reducing their influence and show that this improves classification results of unsupervisedly learned cell representations.

Authors 9

1. Philipp Gräbel Aachen Institute of Imaging & Computer Vision

   RWTH Aachen University

   Affiliation as printed

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

2. Ina Laube Aachen Institute of Imaging & Computer Vision

   RWTH Aachen University

   Affiliation as printed

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

3. Martina Margrit Crysandt Aachen Department of Hematology and Oncology

   Universitätsklinikum Aachen · RWTH Aachen University

   Affiliation as printed

   Dept. of Hematology and Oncology, University Hospital RWTH Aachen University, Germany

4. Reinhild Herwartz Aachen Department of Hematology and Oncology

   Universitätsklinikum Aachen · RWTH Aachen University

   Affiliation as printed

   Dept. of Hematology and Oncology, University Hospital RWTH Aachen University, Germany

5. Melanie Baumann Aachen Department of Hematology and Oncology

   Universitätsklinikum Aachen · RWTH Aachen University

   Affiliation as printed

   Dept. of Hematology and Oncology, University Hospital RWTH Aachen University, Germany

6. Barbara M. Klinkhammer Aachen Institute of Pathology

   Universitätsklinikum Aachen · RWTH Aachen University

   Affiliation as printed

   Institute of Pathology, University Hospital RWTH Aachen University, Germany

7. Peter Boor Aachen Institute of Pathology

   Universitätsklinikum Aachen · RWTH Aachen University

   Affiliation as printed

   Institute of Pathology, University Hospital RWTH Aachen University, Germany

8. Tim H. Brümmendorf Aachen Department of Hematology and Oncology

   Universitätsklinikum Aachen · RWTH Aachen University

   Affiliation as printed

   Dept. of Hematology and Oncology, University Hospital RWTH Aachen University, Germany

9. Dorit Merhof Aachen Institute of Imaging & Computer Vision

   RWTH Aachen University

   Affiliation as printed

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