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Robust Bone Marrow Cell Discrimination by Rotation-Invariant Training of Multi-class Echo State Networks


Kainz, Philipp; Burgsteiner, Harald; Asslaber, Martin; Ahammer, Helmut (2015). Robust Bone Marrow Cell Discrimination by Rotation-Invariant Training of Multi-class Echo State Networks. In: Lazaros, Iliadis; Chrisina, Jayne. Communications in Computer and Information Science. Springer: Springer, 390-400.

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.

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.

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Additional indexing

Item Type:Book Section, not refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Neuroinformatics
Dewey Decimal Classification:570 Life sciences; biology
Language:English
Date:2015
Deposited On:09 Feb 2016 15:50
Last Modified:05 Apr 2016 20:04
Publisher:Springer
Additional Information:Chapter Engineering Applications of Neural Networks
Publisher DOI:https://doi.org/10.1007/978-3-319-23983-5_36

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