  Ultrasound (US) is a critical modality for diagnosing liver fibrosis.
Unfortunately, assessment is very subjective, motivating automated approaches.
We introduce a principled deep convolutional neural network (CNN) workflow that
incorporates several innovations. First, to avoid overfitting on non-relevant
image features, we force the network to focus on a clinical region of interest
(ROI), encompassing the liver parenchyma and upper border. Second, we introduce
global heteroimage fusion (GHIF), which allows the CNN to fuse features from
any arbitrary number of images in a study, increasing its versatility and
flexibility. Finally, we use 'style'-based view-specific parameterization (VSP)
to tailor the CNN processing for different viewpoints of the liver, while
keeping the majority of parameters the same across views. Experiments on a
dataset of 610 patient studies (6979 images) demonstrate that our pipeline can
contribute roughly 7% and 22% improvements in partial area under the curve and
recall at 90% precision, respectively, over conventional classifiers,
validating our approach to this crucial problem.
