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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LATEST: ,ocal ,dap,iv, and ,equential ,raining for Tissue Segmentation of Isointense Infant Brain MRw tissue contrast caused by ongoing myelination processes. In this work, we propose a novel learning method based on Local AdapTivE and Sequential Training (LATEST) for segmentation. Specifically, random forest technique is employed to train a . (a single decision tree) for each voxel in the common

Expiration

Landmark-Based Alzheimer’s Disease Diagnosis Using Longitudinal Structural MR Imagesnear registration or tissue segmentation in the application stage and is robust to the inconsistency among longitudinal scans. Specifically, (1) the discriminative landmarks are first automatically discovered from the whole brain, which can be efficiently localized using a fast landmark detection me

Lineage

Inferring Disease Status by Non-parametric Probabilistic Embedding However, robust and efficient computation of pairwise similarity is a challenging task for large-scale medical image datasets. We specifically target diseases where multiple subtypes of pathology present simultaneously, rendering the definition of the similarity a difficult task. To define pairwise

barium-study

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Explaining Radiological Emphysema Subtypes with Unsupervised Texture Prototypes: MESA COPD Studycan help with the diagnosis of chronic obstructive pulmonary disease (COPD). Automated texture-based quantification of emphysema subtypes has been successfully implemented via supervised learning of these three emphysema subtypes. In this work, we demonstrate that unsupervised learning on a large he

biosphere

幻想

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Automatic Detection of Histological Artifacts in Mouse Brain Slice Imageshistological artifacts, like tissue tears and losses. These artifacts are often produced from manual sample preparation processes, which are ubiquitous in most neuroanatomical laboratories. We present a novel geometric algorithm to automatically detect these artifacts (damage regions) in mouse brain

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Representation Learning for Cross-Modality Classificationion learning approaches, based on autoencoders, that address this problem by learning representations that are similar across domains. Both approaches use, next to the data representation objective, a similarity objective to minimise the difference between representations of corresponding patches fr