论文标题
是什么使转移学习工作可用于医学图像：功能再利用和其他因素
What Makes Transfer Learning Work For Medical Images: Feature Reuse & Other Factors
论文作者
论文摘要
转移学习是一种将知识从一个领域转移到另一个领域的标准技术。对于医学成像中的应用，尽管域之间的任务和图像特征有所不同，但从Imagenet转移已成为事实上的方法。但是，目前尚不清楚哪些因素决定了哪些因素以及在何种程度上转移学习到医疗领域是有用的。最近，人们对源域重复使用的特征的长期假设最近受到质疑。通过在几个医学图像基准数据集上进行的一系列实验，我们探讨了传输学习，数据大小，模型的容量和电感偏差以及源和目标域之间的距离之间的关系。我们的发现表明，在大多数情况下，转移学习是有益的，我们表征了重要的作用功能在成功中的重要作用。
Transfer learning is a standard technique to transfer knowledge from one domain to another. For applications in medical imaging, transfer from ImageNet has become the de-facto approach, despite differences in the tasks and image characteristics between the domains. However, it is unclear what factors determine whether - and to what extent - transfer learning to the medical domain is useful. The long-standing assumption that features from the source domain get reused has recently been called into question. Through a series of experiments on several medical image benchmark datasets, we explore the relationship between transfer learning, data size, the capacity and inductive bias of the model, as well as the distance between the source and target domain. Our findings suggest that transfer learning is beneficial in most cases, and we characterize the important role feature reuse plays in its success.
