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.
