  Cell detection is an important task in biomedical research. Recently, deep
learning methods have made it possible to improve the performance of cell
detection. However, a detection network trained with training data under a
specific condition (source domain) may not work well on data under other
conditions (target domains), which is called the domain shift problem. In
particular, cells are cultured under different conditions depending on the
purpose of the research. Characteristics, e.g., the shapes and density of the
cells, change depending on the conditions, and such changes may cause domain
shift problems. Here, we propose an unsupervised domain adaptation method for
cell detection using a pseudo-cell-position heatmap, where the cell centroid is
at the peak of a Gaussian distribution in the map and selective
pseudo-labeling. In the prediction result for the target domain, even if the
peak location is correct, the signal distribution around the peak often has a
non-Gaussian shape. The pseudo-cell-position heatmap is thus re-generated using
the peak positions in the predicted heatmap to have a clear Gaussian shape. Our
method selects confident pseudo-cell-position heatmaps based on uncertainty and
curriculum learning. We conducted numerous experiments showing that, compared
with the existing methods, our method improved detection performance under
different conditions.
