  A novel hierarchical model is introduced to solve a general problem of
detecting groups of similar objects. Under this model, detection of groups is
performed in hierarchically organized layers while each layer represents a
scope for target objects. The processing of these layers involves sequential
extraction of appearance features for an individual object, consistency
measurement features for nearby objects, and finally the distribution features
for all objects within the group. Using the concept of scope-based
normalization, the extracted features not only enhance local contrast of an
individual object, but also provide consistent characterization for all related
objects. As an example, a microcalcification group detection system for 2D
mammography was developed, and then the learned model was transferred to 3D
digital breast tomosynthesis without any retraining or fine-tuning. The
detection system demonstrated state-of-the-art performance and detected 96% of
cancerous lesions at the rate of 1.2 false positives per volume as measured on
an independent tomosynthesis test set.
