Cluster Entropy: Active Domain Adaptation in Pathological Image Segmentation
This addresses domain adaptation for pathological segmentation, which is crucial for medical applications but is incremental as it builds on existing semi-supervised methods.
The paper tackles domain shift in pathological image segmentation by proposing a cluster entropy method to select effective whole slide images for semi-supervised domain adaptation, achieving competitive results on datasets from two hospitals.
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.