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• Existing semi-supervised domain adaptation methods struggle with severity classification due to unclear class boundaries.limitation 85%
• Severity classification involves naturally ordered class labels, which complicates domain adaptation.technical 90%
• The proposed method aligns source and target domains using rank scores learned via ranking with class order.method 90%
• Cross-Domain Ranking ranks sample pairs across domains as part of the proposed method.method 90%
• Continuous Distribution Alignment aligns rank score distributions between domains as part of the proposed method.method 90%
• Experiments on ulcerative colitis classification validate the effectiveness of the proposed approach.result 85%
• Experiments on diabetic retinopathy classification validate the effectiveness of the proposed approach.result 85%
• The proposed method demonstrates successful alignment of class-specific rank score distributions.result 85%

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