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In this work, we explore the use of a learning-based framework for retrieval of relevant mammogram images from a database, for purposes of aiding diagnoses. A fundamental issue is how to characterize the notion of similarity between images for use in assessing relevance of images in the database. We investigate the use of several learning algorithms, namely, neural networks and support vector machines, in a two-stage hierarchical learning network for predicting the perceptual similarity from similarity scores collected in human-observer studies. The proposed approach is demonstrated using microcalcification clusters extracted from a database consisting of 76 mammograms. Initial results demonstrate that the proposed two-stage hierarchical learning network outperforms a single-stage learning network.