用相似性原型提升跨模态分割的无监督域适应性能
Unsupervised Domain Adaptation via Similarity-based Prototypes for Cross-Modality Segmentation
- 在嵌入空间中学习类别原型，通过相似性约束保证代表性与可分性
- 引入词典存储多图原型，缓解类别遗漏问题，提升对比学习效果
- 适用于标注成本高的跨模态医学图像分割任务
深度学习在各类视觉任务中取得显著成功，但模型在未见数据上常因领域偏移导致性能急剧下降。为减少领域差距并避免昂贵的未见领域标注，本文提出一种基于相似性原型的跨模态分割无监督域适应框架。具体而言，在嵌入空间中学习类别专属原型，并引入相似性约束，使原型对同一语义类具有代表性且与其他类别可分离。同时，使用词典存储来自不同图像的原型，有效缓解类别遗漏问题，支持原型的对比学习，进一步提升性能。大量实验表明，该方法优于现有最先进方法。
原文摘要 · Abstract (English)
Deep learning models have achieved great success on various vision challenges, but a well-trained model would face drastic performance degradation when applied to unseen data. Since the model is sensitive to domain shift, unsupervised domain adaptation attempts to reduce the domain gap and avoid costly annotation of unseen domains. This paper proposes a novel framework for cross-modality segmentation via similarity-based prototypes. In specific, we learn class-wise prototypes within an embedding space, then introduce a similarity constraint to make these prototypes representative for each semantic class while separable from different classes. Moreover, we use dictionaries to store prototypes extracted from different images, which prevents the class-missing problem and enables the contrastive learning of prototypes, and further improves performance. Extensive experiments show that our method achieves better results than other state-of-the-art methods.
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