Guided Representation Learning for the Classification of Hematopoietic Cells

Graebel P, Crysandt M, Klinkhammer BM, Boor P, Bruemmendorf TH, Merhof D (2021)

Publication Type: Conference contribution

Publication year: 2021

Publisher: Institute of Electrical and Electronics Engineers Inc.

Book Volume: 2021-October

Pages Range: 545-551

Conference Proceedings Title: Proceedings of the IEEE International Conference on Computer Vision

Event location: Virtual, Online, CAN

ISBN: 9781665401913

DOI: 10.1109/ICCVW54120.2021.00067

Abstract

Cell classification in human bone marrow microscopy images is a challenging image analysis task due to the number and inter-connection of cell types. While machine learning techniques have vastly higher throughput and could thus be more reliable, humans are intrinsically capable of understanding relations between cell types. In this paper, we propose methods to incorporate such intrinsic model knowledge based on representation learning. To this end, we construct a manually defined, two-dimensional reference embedding, coined embedding guide, which we use together with inverse dimensionality reduction, a distance-based loss and a growing embedding technique. Results show improved classification scores as well as a visually interpretable and clearly defined embedding space.

Involved external institutions

Rheinisch-Westfälische Technische Hochschule (RWTH) Aachen DE Germany (DE) Universitätsklinikum Aachen (UKA) DE Germany (DE)

How to cite

APA:

Graebel, P., Crysandt, M., Klinkhammer, B.M., Boor, P., Bruemmendorf, T.H., & Merhof, D. (2021). Guided Representation Learning for the Classification of Hematopoietic Cells. In Proceedings of the IEEE International Conference on Computer Vision (pp. 545-551). Virtual, Online, CAN: Institute of Electrical and Electronics Engineers Inc..

MLA:

Graebel, Philipp, et al. "Guided Representation Learning for the Classification of Hematopoietic Cells." Proceedings of the 18th IEEE/CVF International Conference on Computer Vision Workshops, ICCVW 2021, Virtual, Online, CAN Institute of Electrical and Electronics Engineers Inc., 2021. 545-551.

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