  Transfer learning is widely used for training machine learning models. Here,
we study the role of transfer learning for training fully convolutional
networks (FCNs) for medical image segmentation. Our experiments show that
although transfer learning reduces the training time on the target task, the
improvement in segmentation accuracy is highly task/data-dependent. Larger
improvements in accuracy are observed when the segmentation task is more
challenging and the target training data is smaller. We observe that
convolutional filters of an FCN change little during training for medical image
segmentation, and still look random at convergence. We further show that quite
accurate FCNs can be built by freezing the encoder section of the network at
random values and only training the decoder section. At least for medical image
segmentation, this finding challenges the common belief that the encoder
section needs to learn data/task-specific representations. We examine the
evolution of FCN representations to gain a better insight into the effects of
transfer learning on the training dynamics. Our analysis shows that although
FCNs trained via transfer learning learn different representations than FCNs
trained with random initialization, the variability among FCNs trained via
transfer learning can be as high as that among FCNs trained with random
initialization. Moreover, feature reuse is not restricted to the early encoder
layers; rather, it can be more significant in deeper layers. These findings
offer new insights and suggest alternative ways of training FCNs for medical
image segmentation.
