少样本分类模型的跨域评估：自然图像与组织病理学图像
Cross-Domain Evaluation of Few-Shot Classification Models: Natural Images vs. Histopathological Images
AI总结：
本研究评估了少样本分类模型在自然图像与组织病理学图像间的跨域性能，通过多数据集和多种shot场景实验，揭示了模型的迁移与泛化能力，并提出了跨域优化建议。
摘要：
本研究调查了少样本分类模型在不同领域间的性能，具体为自然图像与组织病理学图像。我们首先在自然图像上训练多个少样本分类模型，并在组织病理学图像上评估其性能。随后，我们在组织病理学图像上训练相同模型并比较其性能。我们整合了四个组织病理学数据集和一个自然图像数据集，并使用一系列最先进的分类技术，在5-way 1-shot、5-way 5-shot和5-way 10-shot场景下评估了性能。实验结果揭示了少样本分类模型在不同图像领域间的可迁移性与泛化能力。我们分析了这些模型在适应新领域时的优势与局限，并提供了在跨域场景中优化其性能的建议。本研究有助于增进对跨不同领域图像分类背景下少样本学习的理解。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
