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Mammogram retrieval based on incremental learning

In this work we explore a relevance feedback approach in a learning-based framework for retrieval of relevant mammogram images from a database, for purposes of aiding diagnoses. Our goal is to adapt online the learning procedure in accordance with a user's response without the need to repeat the training procedure. Toward this end we develop a relevance feedback approach based on the concept of incremental learning recently developed in the theory of support vector machines. The proposed approach is demonstrated using clustered microcalcifications extracted from a database consisting of 76 mammograms.