Recent publications from the Alzheimer's Disease Neuroimaging Initiative: Reviewing progress toward improved AD clinical trials
MW Weiner, DP Veitch, PS Aisen, LA Beckett… - Alzheimer's & …, 2017 - Elsevier
Abstract Introduction The Alzheimer's Disease Neuroimaging Initiative (ADNI) has continued
development and standardization of methodologies for biomarkers and has provided an …
development and standardization of methodologies for biomarkers and has provided an …
Radiological images and machine learning: trends, perspectives, and prospects
The application of machine learning to radiological images is an increasingly active
research area that is expected to grow in the next five to ten years. Recent advances in …
research area that is expected to grow in the next five to ten years. Recent advances in …
Deep MRI brain extraction: A 3D convolutional neural network for skull stripping
Brain extraction from magnetic resonance imaging (MRI) is crucial for many neuroimaging
workflows. Current methods demonstrate good results on non-enhanced T1-weighted …
workflows. Current methods demonstrate good results on non-enhanced T1-weighted …
Multimodal neuroimaging feature learning with multimodal stacked deep polynomial networks for diagnosis of Alzheimer's disease
The accurate diagnosis of Alzheimer's disease (AD) and its early stage, ie, mild cognitive
impairment, is essential for timely treatment and possible delay of AD. Fusion of multimodal …
impairment, is essential for timely treatment and possible delay of AD. Fusion of multimodal …
Hierarchical feature representation and multimodal fusion with deep learning for AD/MCI diagnosis
For the last decade, it has been shown that neuroimaging can be a potential tool for the
diagnosis of Alzheimer's Disease (AD) and its prodromal stage, Mild Cognitive Impairment …
diagnosis of Alzheimer's Disease (AD) and its prodromal stage, Mild Cognitive Impairment …
Effective feature learning and fusion of multimodality data using stage‐wise deep neural network for dementia diagnosis
In this article, the authors aim to maximally utilize multimodality neuroimaging and genetic
data for identifying Alzheimer's disease (AD) and its prodromal status, Mild Cognitive …
data for identifying Alzheimer's disease (AD) and its prodromal status, Mild Cognitive …
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 …
Latent representation learning for Alzheimer's disease diagnosis with incomplete multi-modality neuroimaging and genetic data
The fusion of complementary information contained in multi-modality data [eg, magnetic
resonance imaging (MRI), positron emission tomography (PET), and genetic data] has …
resonance imaging (MRI), positron emission tomography (PET), and genetic data] has …
Introducing BASE: the Biomes of Australian Soil Environments soil microbial diversity database
Background Microbial inhabitants of soils are important to ecosystem and planetary
functions, yet there are large gaps in our knowledge of their diversity and ecology. The …
functions, yet there are large gaps in our knowledge of their diversity and ecology. The …
A novel relational regularization feature selection method for joint regression and classification in AD diagnosis
In this paper, we focus on joint regression and classification for Alzheimer's disease
diagnosis and propose a new feature selection method by embedding the relational …
diagnosis and propose a new feature selection method by embedding the relational …