Large margin aggregation of local estimates for medical image classification

Medical images typically exhibit complex feature space distributions due to high intra-class variation and inter-class ambiguity. Monolithic classification models are often problematic. In this study, we propose a novel Large Margin Local Estimate (LMLE) method for medical image classification. In the first step, the reference images are subcategorized, and local estimates of the test image are computed based on the reference subcategories. In the second step, the local estimates are fused in a large margin model to derive the similarity level between the test image and the reference images, and the test image is classified accordingly. For evaluation, the LMLE method is applied to classify image patches of different interstitial lung disease (ILD) patterns on high-resolution computed tomography (HRCT) images. We demonstrate promising performance improvement over the state-of-the-art.