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Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls
MR Arbabshirani, S Plis, J Sui, VD Calhoun - Neuroimage, 2017 - Elsevier
Neuroimaging-based single subject prediction of brain disorders has gained increasing
attention in recent years. Using a variety of neuroimaging modalities such as structural …
attention in recent years. Using a variety of neuroimaging modalities such as structural …
Alzheimer's diseases detection by using deep learning algorithms: a mini-review
S Al-Shoukry, TH Rassem, NM Makbol - IEEE Access, 2020 - ieeexplore.ieee.org
The accurate diagnosis of Alzheimer's disease (AD) plays an important role in patient
treatment, especially at the disease's early stages, because risk awareness allows the …
treatment, especially at the disease's early stages, because risk awareness allows the …
Convolutional neural networks-based MRI image analysis for the Alzheimer's disease prediction from mild cognitive impairment
Mild cognitive impairment (MCI) is the prodromal stage of Alzheimer's disease (AD).
Identifying MCI subjects who are at high risk of converting to AD is crucial for effective …
Identifying MCI subjects who are at high risk of converting to AD is crucial for effective …
Multimodal and multiscale deep neural networks for the early diagnosis of Alzheimer's disease using structural MR and FDG-PET images
D Lu, K Popuri, GW Ding, R Balachandar, MF Beg - Scientific reports, 2018 - nature.com
Alzheimer's Disease (AD) is a progressive neurodegenerative disease where biomarkers for
disease based on pathophysiology may be able to provide objective measures for disease …
disease based on pathophysiology may be able to provide objective measures for disease …
Joint classification and regression via deep multi-task multi-channel learning for Alzheimer's disease diagnosis
In the field of computer-aided Alzheimer's disease (AD) diagnosis, jointly identifying brain
diseases and predicting clinical scores using magnetic resonance imaging (MRI) have …
diseases and predicting clinical scores using magnetic resonance imaging (MRI) have …
volBrain: an online MRI brain volumetry system
The amount of medical image data produced in clinical and research settings is rapidly
growing resulting in vast amount of data to analyze. Automatic and reliable quantitative …
growing resulting in vast amount of data to analyze. Automatic and reliable quantitative …
Morphological feature visualization of Alzheimer's disease via multidirectional perception GAN
The diagnosis of early stages of Alzheimer's disease (AD) is essential for timely treatment to
slow further deterioration. Visualizing the morphological features for early stages of AD is of …
slow further deterioration. Visualizing the morphological features for early stages of AD is of …
Machine learning framework for early MRI-based Alzheimer's conversion prediction in MCI subjects
E Moradi, A Pepe, C Gaser, H Huttunen, J Tohka… - Neuroimage, 2015 - Elsevier
Mild cognitive impairment (MCI) is a transitional stage between age-related cognitive
decline and Alzheimer's disease (AD). For the effective treatment of AD, it would be …
decline and Alzheimer's disease (AD). For the effective treatment of AD, it would be …
Deep ensemble learning of sparse regression models for brain disease diagnosis
Recent studies on brain imaging analysis witnessed the core roles of machine learning
techniques in computer-assisted intervention for brain disease diagnosis. Of various …
techniques in computer-assisted intervention for brain disease diagnosis. Of various …
Disease-image-specific learning for diagnosis-oriented neuroimage synthesis with incomplete multi-modality data
Incomplete data problem is commonly existing in classification tasks with multi-source data,
particularly the disease diagnosis with multi-modality neuroimages, to track which, some …
particularly the disease diagnosis with multi-modality neuroimages, to track which, some …