提升皮肤病变分类模型在不同临床条件下的鲁棒性
Contrastive meta-domain adaptation for robust skin lesion classification across clinical and acquisition conditions
- 基于视觉元域概念，迁移大尺度皮肤图像特征
- 跨数据集实验显示分类准确率显著提升，域间差距缩小
- 适合部署于真实临床环境的医学影像分析系统
用于皮肤病图像分析的深度学习模型仍对采集差异和特定域的视觉特征敏感，导致在临床环境中部署时性能下降。我们研究了视觉伪影和域偏移对基于深度学习的皮肤病变分类的影响。提出一种基于视觉元域概念的适配策略，将大型皮肤镜数据集中的视觉表征迁移到临床图像域中，从而提升泛化鲁棒性。在多个皮肤科数据集上的实验表明，分类性能持续提升，且皮肤镜与临床图像之间的差距显著减小。结果强调了面向域的训练对于可部署系统的重要性。
原文摘要 · Abstract (English)
Deep learning models for dermatological image analysis remain sensitive to acquisition variability and domain-specific visual characteristics, leading to performance degradation when deployed in clinical settings. We investigate how visual artifacts and domain shifts affect deep learning-based skin lesion classification. We propose an adaptation strategy, grounded in the idea of visual meta-domains, that transfers visual representations from larger dermoscopic datasets into clinical image domains, thereby improving generalization robustness. Experiments across multiple dermatology datasets show consistent gains in classification performance and reduced gaps between dermoscopic and clinical images. These results emphasize the importance of domain-aware training for deployable systems.
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