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The analysis of cells in bone marrow microscopy images is essential for the diagnosis of many hematopoietic diseases such as leukemia. Automating detection, classification and quantification of different types of leukocytes in whole slide images could improve throughput and reliability. However, variations in the staining agent used to highlight cell features can reduce the accuracy of these methods. In histopathology, data augmentation and normalization techniques are used to make neural networks more robust but their application to hematological image data needs to be investigated. In this paper, we compare six promising approaches on three image sets with different staining characteristics in terms of detection and classification.