  The domain shift problem is an important issue in automatic cell detection. A
detection network trained with training data under a specific condition (source
domain) may not work well in data under other conditions (target domain). We
propose an unsupervised domain adaptation method for cell detection using the
pseudo-cell-position heatmap, where a cell centroid becomes a peak with a
Gaussian distribution in the map. In the prediction result for the target
domain, even if a peak location is correct, the signal distribution around the
peak often has anon-Gaussian shape. The pseudo-cell-position heatmap is
re-generated using the peak positions in the predicted heatmap to have a clear
Gaussian shape. Our method selects confident pseudo-cell-position heatmaps
using a Bayesian network and adds them to the training data in the next
iteration. The method can incrementally extend the domain from the source
domain to the target domain in a semi-supervised manner. In the experiments
using 8 combinations of domains, the proposed method outperformed the existing
domain adaptation methods.
