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Curriculum Feature Alignment Domain Adaptation for Epithelium-Stroma Classification in Histopathological Images | IEEE Journals & Magazine | IEEE Xplore

Curriculum Feature Alignment Domain Adaptation for Epithelium-Stroma Classification in Histopathological Images


Abstract:

In recent years, deep learning methods have received more attention in epithelial-stroma (ES) classification tasks. Traditional deep learning methods assume that the trai...Show More

Abstract:

In recent years, deep learning methods have received more attention in epithelial-stroma (ES) classification tasks. Traditional deep learning methods assume that the training and test data have the same distribution, an assumption that is seldom satisfied in complex imaging procedures. Unsupervised domain adaptation (UDA) transfers knowledge from a labelled source domain to a completely unlabeled target domain, and is more suitable for ES classification tasks to avoid tedious annotation. However, existing UDA methods for this task ignore the semantic alignment across domains. In this paper, we propose a Curriculum Feature Alignment Network (CFAN) to gradually align discriminative features across domains through selecting effective samples from the target domain and minimizing intra-class differences. Specifically, we developed the Curriculum Transfer Strategy (CTS) and Adaptive Centroid Alignment (ACA) steps to train our model iteratively. We validated the method using three independent public ES datasets, and experimental results demonstrate that our method achieves better performance in ES classification compared with commonly used deep learning methods and existing deep domain adaptation methods.
Published in: IEEE Journal of Biomedical and Health Informatics ( Volume: 25, Issue: 4, April 2021)
Page(s): 1163 - 1172
Date of Publication: 03 September 2020

ISSN Information:

PubMed ID: 32881698

Funding Agency:


References

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