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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 thatincorporates several innovations. First, to avoid overfitting on non-relevantimage features, we force the network to focus on a clinical region of interest(ROI), encompassing the liver parenchyma and upper border. Second, we introduceglobal heteroimage fusion (GHIF), which allows the CNN to fuse features fromany arbitrary number of images in a study, increasing its versatility andflexibility. Finally, we use 'style'-based view-specific parameterization (VSP)to tailor the CNN processing for different viewpoints of the liver, whilekeeping the majority of parameters the same across views. Experiments on adataset of 610 patient studies (6979 images) demonstrate that our pipeline cancontribute roughly 7% and 22% improvements in partial area under the curve andrecall at 90% precision, respectively, over conventional classifiers,validating our approach to this crucial problem.

Reliable Liver Fibrosis Assessment from Ultrasound using Global Hetero-Image Fusion and View-Specific Parameterization