  End-to-end deep learning improves breast cancer classification on
diffusion-weighted MR images (DWI) using a convolutional neural network (CNN)
architecture. A limitation of CNN as opposed to previous model-based approaches
is the dependence on specific DWI input channels used during training. However,
in the context of large-scale application, methods agnostic towards
heterogeneous inputs are desirable, due to the high deviation of scanning
protocols between clinical sites. We propose model-based domain adaptation to
overcome input dependencies and avoid re-training of networks at clinical sites
by restoring training inputs from altered input channels given during
deployment. We demonstrate the method's significant increase in classification
performance and superiority over implicit domain adaptation provided by
training-schemes operating on model-parameters instead of raw DWI images.
