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Titlebook: Medical Computer Vision and Bayesian and Graphical Models for Biomedical Imaging; MICCAI 2016 Internat Henning Müller,B. Michael Kelm,Annem

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Lung Nodule Classification by Jointly Using Visual Descriptors and Deep Featuresfour state-of-the-art nodule classification approaches on the benchmark LIDC-IDRI dataset. Our results suggest that the proposed CFBC algorithm can distinguish malignant lung nodules from benign ones more accurately than other four methods.
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Guideline-Based Machine Learning for Standard Plane Extraction in 3D Cardiac Ultrasound for refining each plane. The proposed method was evaluated on a 3D cardiac ultrasound dataset. Compared with other plane extraction methods, it demonstrated an improved accuracy with a significantly faster running time of 0.8 s per volume.
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LATEST: ,ocal ,dap,iv, and ,equential ,raining for Tissue Segmentation of Isointense Infant Brain MRgrouped together to form a forest. Moreover, the estimated probabilities are further used as additional source images to train the next set of local classifiers for refining tissue classification. By iteratively training the subsequent classifiers based on the updated tissue probability maps, . can be built for accurate tissue segmentation.
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A Statistical Model for Simultaneous Template Estimation, Bias Correction, and Registration of 3D Bred to rely on either bias-invariant similarity measures or robust image normalization. Results on synthetic and real brain MRI images demonstrate the capability of the model to capture heterogeneity in intensities and provide a reliable template estimation from registration.
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BigBrain: Automated Cortical Parcellation and Comparison with Existing Brain Atlasesntegration. A 3D atlas defined upon fine-grain anatomical detail of cortical layers and cells is necessary to fully understand neurobiological processes. “BigBrain,” a high-resolution 3D model of a human brain at nearly cellular resolution, was released in 2013. This unique dataset enables the extra
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