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.

利用卷积神经网络对乳腺 X 光造影图像进行分类，提出了一种高性能组织评估方法，采用局部组织分类器的方法在不同尺度上对可疑发现的组织块进行分类，使 AUC 超过了 0.9，并使用该方法生成了适用于临床设置的显著性图像。