Titlebook: Medical Image Computing and Computer Assisted Intervention − MICCAI 2017; 20th International C Maxime Descoteaux,Lena Maier-Hein,Simon Duch

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Deep Multi-task Multi-channel Learning for Joint Classification and Regression of Brain Statusmagnetic resonance imaging (MRI) data, since these two tasks are highly correlated. Although several joint learning models have been developed, most existing methods focus on using human-engineered features extracted from MRI data. Due to the possible heterogeneous property between human-engineered

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Nonlinear Feature Space Transformation to Improve the Prediction of MCI to AD Conversion to target the disease process early. In this paper, we present a novel nonlinear feature transformation scheme to improve the prediction of MCI-AD conversion through semi-supervised learning. Utilizing Laplacian SVM (LapSVM) as a host classifier, the proposed method learns a smooth spatially varyin

Delirium

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Latent Processes Governing Neuroanatomical Change in Aging and Dementiamodate neural systems with high susceptibility to deleterious factors. Due to the overlap, the separation between aging and pathological processes is challenging when analyzing brain structures independently. We propose to identify multivariate latent processes that govern cross-sectional and longit

听觉

A Multi-armed Bandit to Smartly Select a Training Set from Big Medical Datadifferent datasets. Simply including all the data does not only incur high processing costs but can even harm the prediction. We formulate the smart and efficient selection of a training dataset from big medical image data as a multi-armed bandit problem, solved by Thompson sampling. Our method assu

实现

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Maximum Mean Discrepancy Based Multiple Kernel Learning for Incomplete Multimodality Neuroimaging Dalow-rank matrix completion (.imputing the missing values and unknown labels simultaneously) and multi-task learning (.defining one regression task for each combination of modalities and then learning them jointly), are unable to model the complex data-to-label relationship in AD diagnosis and also i