  Applying deep learning methods to mammography assessment has remained a
challenging topic. Dense noise with sparse expressions, mega-pixel raw data
resolution, lack of diverse examples have all been factors affecting
performance. The lack of pixel-level ground truths have especially limited
segmentation methods in pushing beyond approximately bounding regions. We
propose a classification approach grounded in high performance tissue
assessment as an alternative to all-in-one localization and assessment models
that is also capable of pinpointing the causal pixels. First, the objective of
the mammography assessment task is formalized in the context of local tissue
classifiers. Then, the accuracy of a convolutional neural net is evaluated on
classifying patches of tissue with suspicious findings at varying scales, where
highest obtained AUC is above $0.9$. The local evaluations of one such expert
tissue classifier is used to augment the results of a heatmap regression model
and additionally recover the exact causal regions at high resolution as a
saliency image suitable for clinical settings.
