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A novel hierarchical model is introduced to solve a general problem ofdetecting groups of similar objects. Under this model, detection of groups isperformed in hierarchically organized layers while each layer represents ascope for target objects. The processing of these layers involves sequentialextraction of appearance features for an individual object, consistencymeasurement features for nearby objects, and finally the distribution featuresfor all objects within the group. Using the concept of scope-basednormalization, the extracted features not only enhance local contrast of anindividual object, but also provide consistent characterization for all relatedobjects. As an example, a microcalcification group detection system for 2Dmammography was developed, and then the learned model was transferred to 3Ddigital breast tomosynthesis without any retraining or fine-tuning. Thedetection system demonstrated state-of-the-art performance and detected 96% ofcancerous lesions at the rate of 1.2 false positives per volume as measured onan independent tomosynthesis test set.

Transferring Learned Microcalcification Group Detection from 2D  Mammography to 3D Digital Breast Tomosynthesis Using a Hierarchical Model and  Scope-based Normalization Features