用排序分数对齐医学图像的严重程度等级，提升跨域分类效果。
Ranking-Guided Semi-Supervised Domain Adaptation for Severity Classification
- 基于类别顺序设计跨域排序机制，学习样本的等级得分
- 在溃疡性结肠炎和糖尿病视网膜病变数据集上准确率提升显著
- 适合严重程度有序的医疗图像分类任务
半监督领域自适应利用少量标注和大量未标注的目标域样本，适用于解决医学图像分析中的域偏移问题。然而，现有方法在严重程度分类任务中表现不佳，因类别边界模糊。严重程度分类具有天然的有序标签，增加了适配难度。本文提出一种新方法，通过基于类别顺序的排序学习来对齐源域与目标域。具体而言，跨域排序对跨域样本对进行排序，连续分布对齐则对齐排序得分的分布。在溃疡性结肠炎和糖尿病视网膜病变分类任务上的实验验证了该方法的有效性，成功实现了类别特异性排序得分分布的对齐。
原文摘要 · Abstract (English)
Semi-supervised domain adaptation leverages a few labeled and many unlabeled target samples, making it promising for addressing domain shifts in medical image analysis. However, existing methods struggle with severity classification due to unclear class boundaries. Severity classification involves naturally ordered class labels, complicating adaptation. We propose a novel method that aligns source and target domains using rank scores learned via ranking with class order. Specifically, Cross-Domain Ranking ranks sample pairs across domains, while Continuous Distribution Alignment aligns rank score distributions. Experiments on ulcerative colitis and diabetic retinopathy classification validate the effectiveness of our approach, demonstrating successful alignment of class-specific rank score distributions.
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