Identification and classification of white blood cells in peripheral blood smears is a key step for the diagnosis of hematological malignancies. Different lab procedures, illumination, staining, and microscope settings are some of the factors resulting in domain shifts which hampers the applicability and reusability of machine learning methods when applied to data collected from different sites. In this thesis, we propose an autoencoder to extract unsupervised cross-domain features on three different datasets of single white blood cells. Using a Mask R-CNN architecture as a first step allows the autoencoder to focus on the relevant white blood cell and eliminate artifacts in the image.