  Transfer learning from ImageNet is the go-to approach when applying deep
learning to medical images. The approach is either to fine-tune a pre-trained
model or use it as a feature extractor. Most modern architecture contain batch
normalisation layers, and fine-tuning a model with such layers requires taking
a few precautions as they consist of trainable and non-trainable weights and
have two operating modes: training and inference. Attention is primarily given
to the non-trainable weights used during inference, as they are the primary
source of unexpected behaviour or degradation in performance during transfer
learning. It is typically recommended to fine-tune the model with the batch
normalisation layers kept in inference mode during both training and inference.
In this paper, we pay closer attention instead to the trainable weights of the
batch normalisation layers, and we explore their expressive influence in the
context of transfer learning. We find that only fine-tuning the trainable
weights (scale and centre) of the batch normalisation layers leads to similar
performance as to fine-tuning all of the weights, with the added benefit of
faster convergence. We demonstrate this on a variety of seven publicly
available medical imaging datasets, using four different model architectures.
