Abstract
Classification of cell types in context of the architecture in tissue specimen is the basis of diagnostic pathology and decisions for comprehensive investigations rely on a valid interpretation of tissue morphology. Especially visual examination of bone marrow cells takes a considerable amount of time and inter-observer variability can be remarkable. In this work, we propose a novel rotation-invariant learning scheme for multi-class Echo State Networks (ESNs), which achieves very high performance in automated bone marrow cell classification. Based on representing static images as temporal sequence of rotations, we show how ESNs robustly recognize cells of arbitrary rotations by taking advantage of their short-term memory capacity.
The authors would like to thank Michael Pfeiffer from the Institute of Neuroinformatics, University of Zurich and ETH Zurich, for fruitful discussions.
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Kainz, P., Burgsteiner, H., Asslaber, M., Ahammer, H. (2015). Robust Bone Marrow Cell Discrimination by Rotation-Invariant Training of Multi-class Echo State Networks. In: Iliadis, L., Jayne, C. (eds) Engineering Applications of Neural Networks. EANN 2015. Communications in Computer and Information Science, vol 517. Springer, Cham. https://doi.org/10.1007/978-3-319-23983-5_36
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DOI: https://doi.org/10.1007/978-3-319-23983-5_36
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