  Label-free approaches are attractive in cytological imaging due to their
flexibility and cost efficiency. They are supported by machine learning
methods, which, despite the lack of labeling and the associated lower contrast,
can classify cells with high accuracy where the human observer has little
chance to discriminate cells. In order to better integrate these workflows into
the clinical decision making process, this work investigates the calibration of
confidence estimation for the automated classification of leukocytes. In
addition, different visual explanation approaches are compared, which should
bring machine decision making closer to professional healthcare applications.
Furthermore, we were able to identify general detection patterns in neural
networks and demonstrate the utility of the presented approaches in different
scenarios of blood cell analysis.
