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Using deep neural networks to predict the lineage choice of hematopoietic stem cells from time-lapse microscopy images.

München, Ludwig-Maximilians-Universität & Technische Universität, Bioinformatik, Master-Thesis, 2014, 72 S.
Verlagsversion Volltext DOI
Manuel Kroiss examines the differentiation of hematopoietic stem cells using machine learning methods. This work is based on experiments focusing on the lineage choice of CMPs, the progenitors of HSCs, which either become MEP or GMP cells. The author presents a novel approach to distinguish MEP from GMP cells using machine learning on morphology features extracted from bright field images. He tests the performance of different models and focuses on Recurrent Neural Networks with the latest advances from the field of deep learning. Two different improvements to recurrent networks were tested: Long Short Term Memory (LSTM) cells that are able to remember information over long periods of time, and dropout regularization to prevent overfitting. With his method, Manuel Kroiss considerably outperforms standard machine learning methods without time information like Random Forests and Support Vector Machines.
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Publikationstyp Sonstiges: Hochschulschrift
Typ der Hochschulschrift Masterarbeit
e-ISSN 978-3-658-12879-1
ISBN 978-3-658-12878-4
Quellenangaben Band: , Heft: , Seiten: 72 S. Artikelnummer: , Supplement: ,
Verlag Springer Spektrum
Hochschule Ludwig-Maximilians-Universität & Technische Universität
Hochschulort München
Fakultät Bioinformatik
Begutachtungsstatus