Relevance feedback based on incremental learning for mammogram retrieval

In this work we explore a new technique for relevance feedback 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 user responses without the need to repeat the training procedure. Toward this end we develop a relevance feedback approach based on the concept of incremental learning developed recently in the theory of support vector machines. The proposed approach is demonstrated using clustered microcalcifications extracted from a database consisting of 76 mammograms.