Cell Detection in Domain Shift Problem Using Pseudo-Cell-Position Heatmap
This addresses the issue of cell detection models failing under varying conditions, but it is incremental as it builds on existing domain adaptation techniques.
The authors tackled the domain shift problem in automatic cell detection by proposing an unsupervised domain adaptation method using pseudo-cell-position heatmaps, which outperformed existing methods across 8 domain combinations.
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.