Titlebook: Machine Learning in Medical Imaging; 4th International Wo Guorong Wu,Daoqiang Zhang,Fei Wang Conference proceedings 2013 Springer Internati

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Unsupervised Deep Learning for Hippocampus Segmentation in 7.0 Tesla MR Images,ew characteristics shown in 7.0T images, such as richer structural information and more severe intensity inhomogeneity, raise serious issues for the extraction of distinctive and robust features for accurately segmenting hippocampus in 7.0T images. On the other hand, the hand-crafted image features

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Integrating Multiple Network Properties for MCI Identification,. However, most of the existing methods focus on using only single network property, although combination of multiple network properties such as local connectivity and topological properties may be more powerful. Employing the kernel-based method, we propose a novel classification framework that att

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Learning-Boosted Label Fusion for Multi-atlas Auto-Segmentation,ation (ABAS) methods have shown great promise for getting accurate segmentation results especially when multiple atlases are used. In this work, we aim to further improve the performance of ABAS by integrating it with learning-based segmentation techniques. In particular, the Random Forests (RF) sup

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Volumetric Segmentation of Key Fetal Brain Structures in 3D Ultrasound,y has an advantage of quick acquisition but is yet to demonstrate improvements in clinical workflow. In this paper we propose an automatic technique to segment four important fetal brain structures in 3D ultrasound. The technique is built within a Random Decision Forests framework. Our solution incl

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Sparse Classification with MRI Based Markers for Neuromuscular Disease Categorization,e Imaging (MRI) data. Anatomically meaningful features are extracted from structural data (T1- and T2-weighted MR images) and Diffusion Tensor Imaging (DTI) data, and used to train a new machine learning algorithm, the .-support SVM (.sup-SVM). The .-support regularized SVM has an inherent feature s

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