  The task of classifying mammograms is very challenging because the lesion is
usually small in the high resolution image. The current state-of-the-art
approaches for medical image classification rely on using the de-facto method
for ConvNets - fine-tuning. However, there are fundamental differences between
natural images and medical images, which based on existing evidence from the
literature, limits the overall performance gain when designed with algorithmic
approaches. In this paper, we propose to go beyond fine-tuning by introducing a
novel framework called MorphHR, in which we highlight a new transfer learning
scheme. The idea behind the proposed framework is to integrate
function-preserving transformations, for any continuous non-linear activation
neurons, to internally regularise the network for improving mammograms
classification. The proposed solution offers two major advantages over the
existing techniques. Firstly and unlike fine-tuning, the proposed approach
allows for modifying not only the last few layers but also several of the first
ones on a deep ConvNet. By doing this, we can design the network front to be
suitable for learning domain specific features. Secondly, the proposed scheme
is scalable to hardware. Therefore, one can fit high resolution images on
standard GPU memory. We show that by using high resolution images, one prevents
losing relevant information. We demonstrate, through numerical and visual
experiments, that the proposed approach yields to a significant improvement in
the classification performance over state-of-the-art techniques, and is indeed
on a par with radiology experts. Moreover and for generalisation purposes, we
show the effectiveness of the proposed learning scheme on another large
dataset, the ChestX-ray14, surpassing current state-of-the-art techniques.
