Class-aware multi-source domain adaptation algorithm for medical image analysis using reweighted matrix matching strategy
Huiying Zhang, Yongmeng Li, Lei He, Wenbo Zhang, Yuchen Shen, Lumin Xing
TL;DR
This paper introduces a new algorithm for adapting medical image analysis across multiple sources, improving accuracy by addressing class distribution differences.
Contribution
Proposes CAMSDA-RMM, a novel class-aware multi-source domain adaptation algorithm using reweighted matrix matching.
Findings
The algorithm improves classification accuracy by aligning source and target domains using moment matching.
An adaptive weighting mechanism optimizes contributions from different source domains.
Experiments on chest X-ray datasets show the method outperforms existing approaches.
Abstract
Multi-source domain adaptation leverages complementary knowledge from multiple source domains to enhance transfer effectiveness, making it more suitable for complex medical scenarios compared to single-source domain adaptation. However, most existing studies operate under the assumption that the source and target domains share identical class distributions, leaving the challenge of addressing class shift in multi-source domain adaptation largely unexplored. To address this gap, this study proposes a Class-Aware Multi-Source Domain Adaptation algorithm based on a Reweighted Matrix Matching strategy (CAMSDA-RMM). This algorithm employs a class-aware strategy to strengthen positive transfer effects between similar classes. Additionally, first-order and second-order moment matching strategies are applied to effectively align the source and target domains, while an adaptive weighting…
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Taxonomy
TopicsDomain Adaptation and Few-Shot Learning · COVID-19 diagnosis using AI · Multimodal Machine Learning Applications