AIMC Topic: Neuroimaging
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Scaling Synthetic Brain Data Generation.
IEEE journal of biomedical and health informatics
Feb 10, 2025
The limited availability of diverse, high-quality datasets is a significant challenge in applying deep learning to neuroimaging research. Although synthetic data generation can potentially address this issue, on-the-fly generation is computationally ...
Predicting cognitive decline: Deep-learning reveals subtle brain changes in pre-MCI stage.
The journal of prevention of Alzheimer's disease
Feb 6, 2025
BACKGROUND: Mild cognitive impairment (MCI) and preclinical MCI (e.g., subjective cognitive decline, SCD) are considered risk states of dementia, such as Alzheimer's Disease (AD). However, it is challenging to accurately predict conversion from norma...
DeepCERES: A deep learning method for cerebellar lobule segmentation using ultra-high resolution multimodal MRI.
NeuroImage
Feb 6, 2025
This paper introduces a novel multimodal and high-resolution human brain cerebellum lobule segmentation method. Unlike current tools that operate at standard resolution (1 mm) or using mono-modal data, the proposed method improves cerebellum lobule s...
DeepPrep: an accelerated, scalable and robust pipeline for neuroimaging preprocessing empowered by deep learning.
Nature methods
Feb 6, 2025
Neuroimaging has entered the era of big data. However, the advancement of preprocessing pipelines falls behind the rapid expansion of data volume, causing substantial computational challenges. Here we present DeepPrep, a pipeline empowered by deep le...
Knowledge Distillation Guided Interpretable Brain Subgraph Neural Networks for Brain Disorder Exploration.
IEEE transactions on neural networks and learning systems
Feb 6, 2025
The human brain is a highly complex neurological system that has been the subject of continuous exploration by scientists. With the help of modern neuroimaging techniques, there has been significant progress made in brain disorder analysis. There is ...
Community Graph Convolution Neural Network for Alzheimer's Disease Classification and Pathogenetic Factors Identification.
IEEE transactions on neural networks and learning systems
Feb 6, 2025
As a complex neural network system, the brain regions and genes collaborate to effectively store and transmit information. We abstract the collaboration correlations as the brain region gene community network (BG-CN) and present a new deep learning a...
Detection of Alzheimer Disease in Neuroimages Using Vision Transformers: Systematic Review and Meta-Analysis.
Journal of medical Internet research
Feb 5, 2025
BACKGROUND: Alzheimer disease (AD) is a progressive condition characterized by cognitive decline and memory loss. Vision transformers (ViTs) are emerging as promising deep learning models in medical imaging, with potential applications in the detecti...
M₂DC: A Meta-Learning Framework for Generalizable Diagnostic Classification of Major Depressive Disorder.
IEEE transactions on medical imaging
Feb 4, 2025
Psychiatric diseases are bringing heavy burdens for both individual health and social stability. The accurate and timely diagnosis of the diseases is essential for effective treatment and intervention. Thanks to the rapid development of brain imaging...
Radiomics in glioma: emerging trends and challenges.
Annals of clinical and translational neurology
Feb 3, 2025
Radiomics is a promising neuroimaging technique for extracting and analyzing quantitative glioma features. This review discusses the application, emerging trends, and challenges associated with using radiomics in glioma. Integrating deep learning alg...
Deep-ER: Deep Learning ECCENTRIC Reconstruction for fast high-resolution neurometabolic imaging.
NeuroImage
Feb 1, 2025
INTRODUCTION: Altered neurometabolism is an important pathological mechanism in many neurological diseases and brain cancer, which can be mapped non-invasively by Magnetic Resonance Spectroscopic Imaging (MRSI). Advanced MRSI using non-cartesian comp...
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