聚类熵：病理图像分割中的主动域适应
Cluster Entropy: Active Domain Adaptation in Pathological Image Segmentation
AI总结：
针对病理图像分割中的域偏移问题，提出基于聚类熵选择有效 WSI 的主动半监督域适应方法，以衡量目标域分布覆盖并提升跨医院分割性能。
摘要：
病理分割中的域偏移是一个重要问题：由于图像特征不同，在源域（收集自某一特定医院）上训练的网络在目标域（来自不同医院）中表现不佳。由于病理图像存在类别不平衡以及类别先验不同的问题，典型的无监督域适应方法通过对齐源域和目标域分布并不能取得良好效果。本文提出一种聚类熵，用于选择有效的全切片图像（whole slide image，WSI），并将其用于半监督域适应。该方法通过计算每个聚类的熵，衡量 WSI 的图像特征对整个目标域分布的覆盖程度，从而显著提升域适应性能。在来自两家医院收集的数据集上，该方法取得了与现有先进方法相比具有竞争力的结果。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
