title: Large margin aggregation of local estimates for medical image classification.
abstract: 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.
authors.forename: Yang
authors.initials: Y
authors.lastname: Song
authors.forename: Weidong
authors.initials: W
authors.lastname: Cail
authors.forename: Heng
authors.initials: H
authors.lastname: Huang
authors.forename: Yun
authors.initials: Y
authors.lastname: Zhou
authors.forename: David Dagan
authors.initials: DD
authors.lastname: Feng
authors.forename: Mei
authors.initials: M
authors.lastname: Chen
mesh_terms: D000465:Algorithms; D006801:Humans; D017563:Lung Diseases, Interstitial / Q000000981:diagnostic imaging*; D010363:Pattern Recognition, Automated / Q000379:methods*; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014057:Tomography, X-Ray Computed / Q000379:methods*