Hardware domain adaptation for biomedical imaging

We present a scheme termed Hardware Domain Adaptation that transforms the visual appearance of biomedical images to match that of a given optical system. This allows us to exploit large publicly available datasets for the training of custom machine learning algorithms for inference on data sets captured by a different imaging hardware for the same task. Moreover, this method allows us to train models for lower-quality image datasets that are difficult or impossible to annotate manually. We demonstrate the efficacy of this method by using publicly available data to train an algorithm to identify and count white blood cells in images obtained on our custom hardware.