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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...
Bone texture characterization is important for differentiating osteoporotic and healthy subjects. Automated classification is however very challenging due to the high degree of visual similarity between the two types of images. In this paper, we propose to describe the bone textures by extracting dense sets of local descriptors and encoding them with the improved Fisher vector (IFV). Compared to the...
Image patch classification is an important task in many different medical imaging applications. In this work, we have designed a customized Convolutional Neural Networks (CNN) with shallow convolution layer to classify lung image patches with interstitial lung disease (ILD). While many feature descriptors have been proposed over the past years, they can be quite complicated and domain-specific. Our...
With the increasing amount of image data available for cancer staging and diagnosis, it is clear that content-based image retrieval techniques are becoming more important to assist physicians in making diagnoses and tracking disease. Domain-specific feature descriptors have been previously shown to be effective in the retrieval of lung tumors. This work proposes a method to improve the rotation invariance...
Content-based image retrieval has been suggested as an aid to medical diagnosis. Techniques based on standard feature descriptors, however, might not represent optimally the pathological characteristics in medical images. In this paper, we propose a new approach for medical image retrieval based on pathology-centric feature extraction and representation; and patch-based local feature extraction and...
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