Cluster Entropy: Active Domain Adaptation in Pathological Image Segmentation

TL;DR

This paper introduces a cluster entropy method for semi-supervised domain adaptation in pathological image segmentation, effectively selecting representative images to improve performance across different hospital datasets.

Contribution

The paper proposes a novel cluster entropy technique for effective WSI selection, enhancing semi-supervised domain adaptation in pathological segmentation tasks.

Findings

Achieved competitive results on multi-hospital datasets.

Improved domain adaptation performance over existing methods.

Effectively measures image feature coverage of target domain.

Abstract

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.