用自监督+主动域适应提升皮肤病变分类泛化能力
Enhancing Skin Lesion Classification Generalization with Active Domain Adaptation
- 先用自然图像预训练，再在皮肤病变数据上重训练自监督模型
- 在五种主动域适应方法下，十组数据集均提升分类泛化性能
- 适合跨机构、跨设备的皮肤病变诊断模型开发
我们提出一种结合自监督学习（SSL）与主动域适应（ADA）的方法，以提升皮肤病变分类模型的泛化能力。主要步骤包括：在自然图像数据集上选择一个预训练的SSL模型，随后在所有可用的皮肤病变数据集上进行SSL重训练，接着在带标签的源域数据上微调模型，并在目标域数据上应用五种不同的ADA方法。该方法在十个皮肤病变数据集上进行了评估，验证了其在不同域偏移程度下的泛化改进潜力。
原文摘要 · Abstract (English)
We propose a method to improve the generalization of skin lesion classification models by combining self-supervised learning (SSL) and active domain adaptation (ADA). The main steps of the approach include selection of an SSL pre-trained model on natural image datasets, subsequent SSL retraining on all available skin-lesion datasets, fine-tuning of the model on source domain data with labels, and application of ADA methods on target domain data. The efficacy of the proposed approach is assessed in ten skin lesion datasets with five different ADA methods, demonstrating its potential to improve generalization in settings with different amounts of domain shifts.
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