Ranking-Guided Semi-Supervised Domain Adaptation for Severity Classification
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866914439721123840 |
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| author | Harada, Shota Bise, Ryoma Tanaka, Kiyohito Uchida, Seiichi |
| author_facet | Harada, Shota Bise, Ryoma Tanaka, Kiyohito Uchida, Seiichi |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_01834 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Ranking-Guided Semi-Supervised Domain Adaptation for Severity Classification Harada, Shota Bise, Ryoma Tanaka, Kiyohito Uchida, Seiichi Computer Vision and Pattern Recognition 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. |
| title | Ranking-Guided Semi-Supervised Domain Adaptation for Severity Classification |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2604.01834 |