猫，而不是CAT扫描：2D医学图像分类迁移学习中数据集相似性的研究
Cats, not CAT scans: a study of dataset similarity in transfer learning for 2D medical image classification
摘要：
迁移学习是医学图像分类的常用策略，特别是通过对源数据进行预训练和对目标数据进行微调。关于如何选择合适的源数据，目前还没有达成共识，在文献中，我们可以找到有利于大型自然图像数据集（如ImageNet）的证据，以及有利于更专业的医学数据集的证据。在本文中，我们进行了系统的研究与9个源数据集与自然或医学图像，和三个目标医学数据集，所有与2D图像。我们发现ImageNet是导致最高性能的来源，但更大的数据集不一定更好。我们还研究了数据相似性的不同定义。我们发现，共同的直觉相似性可能是不准确的，因此不足以预测一个合适的源先验。最后，我们讨论了在这一领域进一步研究所需的几个步骤，特别是关于其他类型（例如3D）的医学图像。我们的实验和预训练模型可通过\url{https://www.github.com/vcheplygina/cats-scans}获得
英文摘要：
Transfer learning is a commonly used strategy for medical image classification, especially via pretraining on source data and fine-tuning on target data. There is currently no consensus on how to choose appropriate source data, and in the literature we can find both evidence of favoring large natural image datasets such as ImageNet, and evidence of favoring more specialized medical datasets. In this paper we perform a systematic study with nine source datasets with natural or medical images, and three target medical datasets, all with 2D images. We find that ImageNet is the source leading to the highest performances, but also that larger datasets are not necessarily better. We also study different definitions of data similarity. We show that common intuitions about similarity may be inaccurate, and therefore not sufficient to predict an appropriate source a priori. Finally, we discuss several steps needed for further research in this field, especially with regard to other types (for example 3D) medical images. Our experiments and pretrained models are available via \url{https://www.github.com/vcheplygina/cats-scans}
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。
