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.

翻译：细胞检测是生物医学研究中的重要任务。近年来，深度学习方法已能够提升细胞检测的性能。然而，在特定条件（源域）下使用训练数据训练出的检测网络，在其它条件（目标域）的数据上可能表现不佳，这被称为域偏移问题。特别是，根据研究目的的不同，细胞会在不同的条件下培养。细胞的形态和密度等特征会随条件变化而变化，这种变化可能引发域偏移问题。本文提出了一种用于细胞检测的无监督域适应方法，该方法利用伪细胞位置热图（其中细胞质心位于热图中高斯分布的峰值处）和选择性伪标签。在目标域的预测结果中，即使峰值位置正确，峰值周围的信号分布通常也呈非高斯形状。因此，利用预测热图中的峰值位置重新生成伪细胞位置热图，使其具有清晰的高斯形状。我们的方法基于不确定性和课程学习选择可靠的伪细胞位置热图。大量实验表明，与现有方法相比，我们的方法在不同条件下提高了检测性能。