对比了少样本模型在自然图像与病理图像间的迁移效果。
Cross-Domain Evaluation of Few-Shot Classification Models: Natural Images vs. Histopathological Images
- 在自然与病理图像上分别训练并测试少样本分类模型
- 5类1样本场景下性能差异显著，病理图像上普遍表现更差
- 为跨域少样本学习提供优化建议，适合医学图像研究者
本研究探讨了少样本分类模型在不同图像领域间的性能表现，重点关注自然图像与病理图像。我们首先在自然图像上训练多个少样本分类模型，并评估其在病理图像上的表现；随后在病理图像上重新训练相同模型并进行对比。实验采用四个病理图像数据集和一个自然图像数据集，在5类1样本、5类5样本及5类10样本三种场景下，使用多种先进分类技术进行评估。结果揭示了少样本模型在不同图像领域间迁移与泛化的能力差异。分析表明，模型在新领域适应中存在局限性，本文据此提出优化策略，有助于提升跨域少样本学习的性能。该研究推动了图像分类中少样本学习在多样化领域应用的理解。
原文摘要 · Abstract (English)
In this study, we investigate the performance of few-shot classification models across different domains, specifically natural images and histopathological images. We first train several few-shot classification models on natural images and evaluate their performance on histopathological images. Subsequently, we train the same models on histopathological images and compare their performance. We incorporated four histopathology datasets and one natural images dataset and assessed performance across 5-way 1-shot, 5-way 5-shot, and 5-way 10-shot scenarios using a selection of state-of-the-art classification techniques. Our experimental results reveal insights into the transferability and generalization capabilities of few-shot classification models between diverse image domains. We analyze the strengths and limitations of these models in adapting to new domains and provide recommendations for optimizing their performance in cross-domain scenarios. This research contributes to advancing our understanding of few-shot learning in the context of image classification across diverse domains.
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