Mammogram Retrieval by Similarity Learning from Experts

A key in content-based image retrieval is the definition of similarity measure for comparing a query image with images in a database. In this work, we explore a similarity measure based on supervised learning from expert readers for mammogram retrieval. We evaluate the approach using an observer study with a set of clinical mammograms. Our results demonstrate that the proposed supervised learning approach can be used to model the notion of similarity by expert readers in their interpretation of mammogram images, and can outperform alternative similarity measures derived from unsupervised learning.