Transfer learning from natural image datasets, particularly ImageNet, using
standard large models and corresponding pretrained weights has become a
de-facto method for deep learning applications to medical imaging. However,
there are fundamental differences in data sizes, features and task
specifications between natural image classification and the target medical
tasks, and there is little understanding of the effects of transfer. In this
paper, we explore properties of transfer learning for medical imaging. A
performance evaluation on two large scale medical imaging tasks shows that
surprisingly, transfer offers little benefit to performance, and simple,
lightweight models can perform comparably to ImageNet architectures.
Investigating the learned representations and features, we find that some of
the differences from transfer learning are due to the over-parametrization of
standard models rather than sophisticated feature reuse. We isolate where
useful feature reuse occurs, and outline the implications for more efficient
model exploration. We also explore feature independent benefits of transfer
arising from weight scalings.

本文探讨了用自然图像数据集（ImageNet）通过标准大型模型和预训练权重进行迁移学习在深度学习医学成像应用中的方法。然而，此方法在自然图像分类和目标医学任务之间存在根本差异，并且人们对迁移效果的了解甚少。该研究表明，在两项大规模医学成像任务的性能评估中，迁移学习对性能的提升有限，而简单轻量级模型可以与 ImageNet 架构相当。此外，还探讨了迁移的权重尺度的独立特性，并阐述了对更高效的模型探索的意义。