  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.
