Boosted classification of breast cancer by retrieval of cases having similar disease likelihood
In diagnostic imaging, recent studies have shown that retrieval of cases that are similar to the case being evaluated can boost its classification performance. In this work we investigate how to improve the utility of the retrieved cases by considering the similarity both in the image features and in the pathology when comparing the cases. To demonstrate the benefit of this retrieval strategy, we propose a boosted Adaboost classifier which can be adapted to the retrieved cases at a low computational cost. The proposed approach was tested on a set of 981 mammogram cases (449 malignant, 532 benign). The results show that the retrieval-boosted Adaboost classifier can significantly outperform its baseline counterpart, and that inclusion of pathology information (measured by the likelihood of malignancy) in the retrieval can further improve the classification accuracy.