The domain shift in pathological segmentation is an important problem, where a network trained by a source domain (collected at a specific hospital) does not work well in the target domain (from different hospitals) due to the different image features. Due to the problems of class imbalance and different class prior of pathology, typical unsupervised domain adaptation methods do not work well by aligning the distribution of source domain and target domain. In this paper, we propose a cluster entropy for selecting an effective whole slide image (WSI) that is used for semi-supervised domain adaptation. This approach can measure how the image features of the WSI cover the entire distribution of the target domain by calculating the entropy of each cluster and can significantly improve the performance of domain adaptation. Our approach achieved competitive results against the prior arts on datasets collected from two hospitals.

翻译：病理分割中的域偏移是一个重要问题，即由源域（特定医院采集）训练的网络因图像特征差异而在目标域（不同医院）表现不佳。由于病理学中类别不平衡和不同类别先验的问题，典型的无监督域自适应方法通过对齐源域和目标域的分布效果不佳。本文提出了一种用于选择有效全切片图像（WSI）的聚类熵方法，并将其应用于半监督域自适应。该方法通过计算每个聚类的熵，衡量WSI图像特征覆盖目标域整体分布的程度，从而显著提升域自适应性能。在两个医院采集的数据集上，我们的方法取得了与先前最优方法竞争的结果。