Abstract
While providing powerful solutions for many problems, deep neural networks require large amounts of training data. In medical image computing, this is a severe limitation, as the required expertise makes annotation efforts often infeasible. This also applies to the automated analysis of hematopoietic cells in bone marrow whole slide images. In this work, we propose approaches to restrict a neural network towards learning of rotation invariant or equivariant representation. Even though the proposed methods achieve this goal, it does not increase classification scores on unsupervisedly learned representations.
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© 2021 Der/die Autor(en), exklusiv lizenziert durch Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature
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Gräbel, P. et al. (2021). Rotation Invariance for Unsupervised Cell Representation Learning. In: Palm, C., Deserno, T.M., Handels, H., Maier, A., Maier-Hein, K., Tolxdorff, T. (eds) Bildverarbeitung für die Medizin 2021. Informatik aktuell. Springer Vieweg, Wiesbaden. https://doi.org/10.1007/978-3-658-33198-6_12
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DOI: https://doi.org/10.1007/978-3-658-33198-6_12
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