Case-adaptive classification based on image retrieval for computer-aided diagnosis

In this work we propose an image-retrieval based approach for case-adaptive classifier design in computer-aided diagnosis (CAD). The traditional approach in CAD is to first train a pattern-classifier based on a set of existing training samples, and then apply this classifier to subsequent new cases. In our proposed approach, we will first apply image-retrieval to obtain a set of lesion images from a library of known cases that have similar image features to a case being diagnosed (i.e., query). These retrieved cases are then used to optimize a pattern-classifier toward boosting its classification accuracy on the query case. In our experiments the proposed retrieval-driven approach was tested on a library of mammogram images from 589 cases (331 benign, 258 malignant), and was demonstrated to yield significant improvement in classification performance.