Regularized adaptive classification based on image retrieval for clustered microcalcifications
We propose a regularization based approach for efficient, case-adaptive classification in computer-aided diagnosis (CAD) of breast cancer. The goal is to boost the classification accuracy on a query case by making use of a set of similar cases retrieved from an existing library of known cases. In the proposed approach, a regularization scheme in the form a prior derived from an existing baseline classifier is used for the adaptive classifier, which can reduce the extra computational burden associated with adaption of the classifier for a query case. We consider two different forms for the regularization prior. In the experiments the proposed approach is demonstrated on a data set of 1,006 clinical cases. The results show that it could achieve improvements in both numerical efficiency and classification performance.