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Cluster Entropy: Active Domain Adaptation in Pathological Image Segmentation

arXiv 2304.13513 ↗First posted 26 Apr 2023cs.CV cs.AI cs.LGcited by 0

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Abstract (arXiv)

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.

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References · 16

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4 more
  1. 2019Diverse mini-batch Active Learningcited by 89
  2. 2020Active Domain Adaptation via Clustering Uncertainty-weighted Embeddingscited by 72
  3. 2020Attract, Perturb, and Explore: Learning a Feature Alignment Network for Semi-supervised Domain Adaptationcited by 69
  4. 2021Multi-Anchor Active Domain Adaptation for Semantic Segmentationcited by 29

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