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Kainz, P; Burgsteiner, H; Asslaber, M; Ahammer, H.
Training echo state networks for rotation-invariant bone marrow cell classification.
Neural Comput Appl. 2017; 28(6):1277-1292 Doi: 10.1007/s00521-016-2609-9 [OPEN ACCESS]
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Führende Autor*innen der Med Uni Graz
Kainz Philipp
Co-Autor*innen der Med Uni Graz
Ahammer Helmut
Asslaber Martin

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The main principle of diagnostic pathology is the reliable interpretation of individual cells in context of the tissue architecture. Especially a confident examination of bone marrow specimen is dependent on a valid classification of myeloid cells. 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 performance of our approach is compared to a classification random forest that learns rotation-invariance in a conventional way by exhaustively training on multiple rotations of individual samples. The methods were evaluated on a human bone marrow image database consisting of granulopoietic and erythropoietic cells in different maturation stages. Our ESN approach to cell classification does not rely on segmentation of cells or manual feature extraction and can therefore directly be applied to image data.

Find related publications in this database (Keywords)
Computer-assisted pathology
Histopathological image analysis
Bone marrow cell classification
Echo state networks
Reservoir computing
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