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This database contains 3 studies, archived under the term: "pattern recognition automated"

Comparing registration methods for mapping brain change using tensor-based morphometry

Measures of brain changes can be computed from sequential MRI scans, providing valuable information on disease progression for neuroscientific studies and clinical trials. Tensor-based morphometry (TBM) creates maps of these brain changes, visualizing the 3D profile and rates of tissue growth or atrophy. In this paper, we examine the power of different nonrigid registration models […]

Computer-assisted system for diagnosis of Alzheimer disease using data base- independent estimation and fluorodeoxyglucose- positron-emission tomography and 3D-stereotactic surface projection

Background and Purpose: Recently, voxel-based statistical parametric images have been developed as additional diagnostic tools for AD. However these methods require the generation of a data base of healthy brain images. The purpose of this study was to produce and evaluate an automatic method using a data base-independent estimation system for the diagnosis of mild […]

Deep learning based imaging data completion for improved brain disease diagnosis

Combining multi-modality brain data for disease diagnosis commonly leads to improved performance. A challenge in using multimodality data is that the data are commonly incomplete; namely, some modality might be missing for some subjects. In this work, we proposed a deep learning based framework for estimating multi-modality imaging data. Our method takes the form of […]

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