Multimodal neuroimaging computing: a review of the applications in neuropsychiatric disorders
Multimodal neuroimaging is increasingly used in neuroscience research, as it overcomes
the limitations of individual modalities. One of the most important applications of multimodal …
the limitations of individual modalities. One of the most important applications of multimodal …
Multimodal neuroimaging feature learning for multiclass diagnosis of Alzheimer's disease
The accurate diagnosis of Alzheimer's disease (AD) is essential for patient care and will be
increasingly important as disease modifying agents become available, early in the course of …
increasingly important as disease modifying agents become available, early in the course of …
Early diagnosis of Alzheimer's disease with deep learning
The accurate diagnosis of Alzheimer's disease (AD) plays a significant role in patient care,
especially at the early stage, because the consciousness of the severity and the progression …
especially at the early stage, because the consciousness of the severity and the progression …
Deep learning in healthcare
D Kaul, H Raju, BK Tripathy - Deep Learning in Data Analytics: Recent …, 2022 - Springer
Abstract Machine learning is quickly becoming an important tool for diagnosis and prognosis
of various medical conditions. Complex input output mappings are dealt in deep learning …
of various medical conditions. Complex input output mappings are dealt in deep learning …
Lung nodule classification with multilevel patch-based context analysis
In this paper, we propose a novel classification method for the four types of lung nodules, ie,
well-circumscribed, vascularized, juxta-pleural, and pleural-tail, in low dose computed …
well-circumscribed, vascularized, juxta-pleural, and pleural-tail, in low dose computed …
Multimodal neuroimaging computing: the workflows, methods, and platforms
The last two decades have witnessed the explosive growth in the development and use of
noninvasive neuroimaging technologies that advance the research on human brain under …
noninvasive neuroimaging technologies that advance the research on human brain under …
Survival analysis for high-dimensional, heterogeneous medical data: Exploring feature extraction as an alternative to feature selection
Background In clinical research, the primary interest is often the time until occurrence of an
adverse event, ie, survival analysis. Its application to electronic health records is challenging …
adverse event, ie, survival analysis. Its application to electronic health records is challenging …
Multi-view feature selection and classification for Alzheimer's disease diagnosis
In our present society, Alzheimer's disease (AD) is the most common dementia form in
elderly people and has been a big social health problem worldwide. In this paper, we …
elderly people and has been a big social health problem worldwide. In this paper, we …
Multi-phase feature representation learning for neurodegenerative disease diagnosis
Feature learning with high dimensional neuroimaging features has been explored for the
applications on neurodegenerative diseases. Low-dimensional biomarkers, such as mental …
applications on neurodegenerative diseases. Low-dimensional biomarkers, such as mental …
Robust Alzheimer's disease classification based on multimodal neuroimaging
DB Akhila, S Shobhana, AL Fred… - 2016 IEEE international …, 2016 - ieeexplore.ieee.org
Alzheimer's disease (AD) is a neurodegenerative disorder which leads to severe brain
damage. The main objective of this work is to diagnosis Alzheimer's disease using Elman …
damage. The main objective of this work is to diagnosis Alzheimer's disease using Elman …