Ranking-Guided Semi-Supervised Domain Adaptation for Severity Classification

Abstract

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.

Figures (3)

• Figure 1: Overview of the proposed method. The top and bottom rows show the feature and ranking space, respectively. The rank score is derived by applying the ranking function $r(\bm{x})$ to the features in the top row. (a) Initial distribution in a semi-supervised domain adaptation scenario, considering class order ($\times \succ \diamond \succ \circ$), and target rank distribution estimation for unlabeled target samples. (b) Association of source and target rank distributions with rank-based soft labels, where arrow size indicates the association weight. (c) Semi-supervised domain adaptation using cross-domain ranking and continuous distribution alignment.
• Figure 2: Effect of Cross-Domain Ranking (CDR). (a) Distribution when learning to rank within the same domain. (b) Distribution with CDR. The dashed lines represent sample pairs.
• Figure 3: Visualization of the rank score distribution in LIMUC$\rightarrow$Private. The horizontal and vertical axes represent the rank score and sample density, respectively.