Surrounding cell suppression for unsupervised representation learning in hematological cell classification
Grabel P, Laube I, Crysandt M, Herwartz R, Baumann M, Klinkhammer BM, Boor P, Bruemmendorf TH, Merhof D (2021)
Publication Type: Conference contribution
Publication year: 2021
Journal
Publisher: IEEE Computer Society
Book Volume: 2021-April
Pages Range: 526-530
Conference Proceedings Title: Proceedings - International Symposium on Biomedical Imaging
Event location: Nice, FRA
ISBN: 9781665412469
DOI: 10.1109/ISBI48211.2021.9433913
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.
Involved external institutions
Germany (DE)How to cite
APA:
Grabel, P., Laube, I., Crysandt, M., Herwartz, R., Baumann, M., Klinkhammer, B.M.,... Merhof, D. (2021). Surrounding cell suppression for unsupervised representation learning in hematological cell classification. In Proceedings - International Symposium on Biomedical Imaging (pp. 526-530). Nice, FRA: IEEE Computer Society.
MLA:
Grabel, Philipp, et al. "Surrounding cell suppression for unsupervised representation learning in hematological cell classification." Proceedings of the 18th IEEE International Symposium on Biomedical Imaging, ISBI 2021, Nice, FRA IEEE Computer Society, 2021. 526-530.
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