Large Margin Local Estimate With Applications to Medical Image Classification

Abstract

Medical images usually exhibit large intra-class variation and inter-class ambiguity in the feature space, which could affect classification accuracy. To tackle this issue, we propose a new Large Margin Local Estimate (LMLE) classification model with sub-categorization based sparse representation. We first sub-categorize the reference sets of different classes into multiple clusters, to reduce feature variation within each subcategory compared to the entire reference set. Local estimates are generated for the test image using sparse representation with reference subcategories as the dictionaries. The similarity between the test image and each class is then computed by fusing the distances with the local estimates in a learning-based large margin aggregation construct to alleviate the problem of inter-class ambiguity. The derived similarities are finally used to determine the class label. We demonstrate that our LMLE model is generally applicable to different imaging modalities, and applied it to three tasks: interstitial lung disease (ILD) classification on high-resolution computed tomography (HRCT) images, phenotype binary classification and continuous regression on brain magnetic resonance (MR) imaging. Our experimental results show statistically significant performance improvements over existing popular classifiers.

Holistic classification of CT attenuation patterns for interstitial lung diseases via deep convolutional neural networks

“…CNN also shows promise in medical image analysis applications, such as mitosis detection (Cireşan et al 2013), lymph node detection (Roth et al 2014) and knee cartilage segmentation (Prasoon et al 2013). In previous ILD classification work, hand-crafted local image descriptors (such as LBP, HOG) are used in Depeursinge, Van de Ville et al (2012), Song et al (2013, 2015) to capture the image patch appearance.…”

“…Obtaining image tags alone is cost effective and can be obtained very efficiently. On the other hand, our new set-up of using holistic images makes it significantly more challenging than the previous settings (Song et al 2013, 2015; Li et al 2014), since the manual ROIs are no longer required. Image patches as classification instances, which are extracted from the annotated ROIs, are well spatially aligned or invariant to their absolute intraslice CT coordinates.…”

“…For computer-aided ILD classification, all previous studies have employed a patch-based image representation with the classification results of moderate success (Depeursinge, Van de Ville et al 2012; Song et al 2013, 2015; Li et al 2014). There are two major drawbacks for the image patch-based methods: (1), The image patch sizes or scales in studies (Song et al 2013, 2015) are relatively small (31 × 31 pixels) where some visual details and spatial context may not be fully captured. The holistic computed tomography (CT) slice holds a lot of details that may be overlooked in the patch-based representation.…”

“…This would be the essential component for a successful and practical medical image analysis tool at a truly large scale. Different from Li et al (2014), Song et al (2013, 2015), our CNN-based method is formulated as a holistic image recognition task (Russakovsky et al 2015) that is also considered as a weakly supervised learning problem. Obtaining image tags alone is cost effective and can be obtained very efficiently.…”

Deep patch-based priors under a fully convolutional encoder-decoder architecture for interstitial lung disease segmentation

“…it includes only some parts of selected image slices out of the whole volumetric dataset. Until now, such databases are exploited within slice [4] or patch-based [5,6] classification paradigms. However, patch-based classification of lung tissue is not very appropriate for disease segmentation since it fails to capture accurate and smooth boundaries between different tissue types.…”