Zusammenfassung
Fast and accurate morphological classification of cells in bone marrow samples is a key step in the diagnostic workup of many disorders of the hematopoietic system such as leukemias. In spite of its long-established key position, morphological examination of bone marrow samples has been difficult to automatise, and is still mainly performed manually by trained cytologists on light microscopes. In our contribution [1], we present a neural network for classification of light microscopy images of bone marrow samples.
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Matek C, Krappe S, Münzenmayer C, Haferlach T, Marr C. Highly accurate differentiation of bone marrow cell morphologies using deep neural networks on a large image data set. Blood. 2021;138(20):1917–27.
Matek C, Krappe S, Münzenmayer C, Haferlach T, Marr C. An expert-annotated reference dataset for bone marrow cytomorphology. The Cancer Imaging Archive (TCIA). 2021.
Choi JW, Ku Y, Yoo BW, Kim JA, Lee DS, Chai YJ et al. White blood cell differential count of maturation stages in bone marrow smear using dual-stage convolutional neural networks. PloS One. 2017;12:e0189259.
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© 2022 Der/die Autor(en), exklusiv lizenziert an Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature
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Matek, C., Krappe, S., Münzenmayer, C., Haferlach, T., Marr, C. (2022). Abstract: A Database and Neural Network for Highly Accurate Classification of Single Bone Marrow Cells. In: Maier-Hein, K., Deserno, T.M., Handels, H., Maier, A., Palm, C., Tolxdorff, T. (eds) Bildverarbeitung für die Medizin 2022. Informatik aktuell. Springer Vieweg, Wiesbaden. https://doi.org/10.1007/978-3-658-36932-3_34
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DOI: https://doi.org/10.1007/978-3-658-36932-3_34
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