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• W3204692365 abstract "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 a non-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." @default.
• W3204692365 created "2021-10-11" @default.
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• W3204692365 date "2021-01-01" @default.
• W3204692365 modified "2023-10-15" @default.
• W3204692365 title "Cell Detection in Domain Shift Problem Using Pseudo-Cell-Position Heatmap" @default.
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• W3204692365 doi "https://doi.org/10.1007/978-3-030-87237-3_37" @default.
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