BIRNet: Brain image registration using dual-supervised fully convolutional networks
In this paper, we propose a deep learning approach for image registration by predicting
deformation from image appearance. Since obtaining ground-truth deformation fields for …
deformation from image appearance. Since obtaining ground-truth deformation fields for …
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 …
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 …
Machine learning for brain imaging genomics methods: a review
In the past decade, multimodal neuroimaging and genomic techniques have been
increasingly developed. As an interdisciplinary topic, brain imaging genomics is devoted to …
increasingly developed. As an interdisciplinary topic, brain imaging genomics is devoted to …
Brain imaging genomics: integrated analysis and machine learning
Brain imaging genomics is an emerging data science field, where integrated analysis of
brain imaging and genomics data, often combined with other biomarker, clinical, and …
brain imaging and genomics data, often combined with other biomarker, clinical, and …
Attention-guided hybrid network for dementia diagnosis with structural MR images
Deep-learning methods (especially convolutional neural networks) using structural magnetic
resonance imaging (sMRI) data have been successfully applied to computer-aided …
resonance imaging (sMRI) data have been successfully applied to computer-aided …
A review of fusion methods for omics and imaging data
The development of omics data and biomedical images has greatly advanced the progress
of precision medicine in diagnosis, treatment, and prognosis. The fusion of omics and …
of precision medicine in diagnosis, treatment, and prognosis. The fusion of omics and …
Multi-modal latent space inducing ensemble SVM classifier for early dementia diagnosis with neuroimaging data
Fusing multi-modality data is crucial for accurate identification of brain disorder as different
modalities can provide complementary perspectives of complex neurodegenerative disease …
modalities can provide complementary perspectives of complex neurodegenerative disease …
Self-calibrated brain network estimation and joint non-convex multi-task learning for identification of early Alzheimer's disease
Detection of early stages of Alzheimer's disease (AD)(ie, mild cognitive impairment (MCI)) is
important to maximize the chances to delay or prevent progression to AD. Brain connectivity …
important to maximize the chances to delay or prevent progression to AD. Brain connectivity …
Assessing clinical progression from subjective cognitive decline to mild cognitive impairment with incomplete multi-modal neuroimages
Accurately assessing clinical progression from subjective cognitive decline (SCD) to mild
cognitive impairment (MCI) is crucial for early intervention of pathological cognitive decline …
cognitive impairment (MCI) is crucial for early intervention of pathological cognitive decline …