[HTML][HTML] Image harmonization: A review of statistical and deep learning methods for removing batch effects and evaluation metrics for effective harmonization
Magnetic resonance imaging and computed tomography from multiple batches (eg sites,
scanners, datasets, etc.) are increasingly used alongside complex downstream analyses to …
scanners, datasets, etc.) are increasingly used alongside complex downstream analyses to …
Robustly uncovering the heterogeneity of neurodegenerative disease by using data-driven subtyping in neuroimaging: a review
Neurodegenerative diseases are associated with heterogeneity in genetics, pathology, and
clinical manifestation. Understanding this heterogeneity is particularly relevant for clinical …
clinical manifestation. Understanding this heterogeneity is particularly relevant for clinical …
Conv-Swinformer: Integration of CNN and shift window attention for Alzheimer's disease classification
Z Hu, Y Li, Z Wang, S Zhang, W Hou… - Computers in Biology …, 2023 - Elsevier
Deep learning (DL) algorithms based on brain MRI images have achieved great success in
the prediction of Alzheimer's disease (AD), with classification accuracy exceeding even that …
the prediction of Alzheimer's disease (AD), with classification accuracy exceeding even that …
Hybrid federated learning with brain-region attention network for multi-center Alzheimer's disease detection
Identifying reproducible and interpretable biomarkers for Alzheimer's disease (AD) detection
remains a challenge. AD detection using multi-center datasets can expand the sample size …
remains a challenge. AD detection using multi-center datasets can expand the sample size …
Altered global signal topography in Alzheimer's disease
Background Alzheimer's disease (AD) is a neurodegenerative disease associated with
widespread disruptions in intrinsic local specialization and global integration in the …
widespread disruptions in intrinsic local specialization and global integration in the …
[HTML][HTML] Disentangling sex-dependent effects of APOE on diverse trajectories of cognitive decline in Alzheimer's disease
Current diagnostic systems for Alzheimer's disease (AD) rely upon clinical signs and
symptoms, despite the fact that the multiplicity of clinical symptoms renders various …
symptoms, despite the fact that the multiplicity of clinical symptoms renders various …
Association between polygenic risk for Alzheimer's disease and brain structure in children and adults
XY He, BS Wu, K Kuo, W Zhang, Q Ma… - Alzheimer's Research & …, 2023 - Springer
Background The correlations between genetic risk for Alzheimer's disease (AD) with
comprehensive brain regions at a regional scale are still not well understood. We aim to …
comprehensive brain regions at a regional scale are still not well understood. We aim to …
Brain network decoupling with increased serum neurofilament and reduced cognitive function in Alzheimer's disease
Neurofilament light chain, a putative measure of neuronal damage, is measurable in blood
and CSF and is predictive of cognitive function in individuals with Alzheimer's disease …
and CSF and is predictive of cognitive function in individuals with Alzheimer's disease …
Deep learning for brain MRI confirms patterned pathological progression in Alzheimer's disease
Deep learning (DL) on brain magnetic resonance imaging (MRI) data has shown excellent
performance in differentiating individuals with Alzheimer's disease (AD). However, the value …
performance in differentiating individuals with Alzheimer's disease (AD). However, the value …
The Alzheimer's Disease Neuroimaging Initiative in the era of Alzheimer's disease treatment: A review of ADNI studies from 2021 to 2022
DP Veitch, MW Weiner, M Miller, PS Aisen… - Alzheimer's & …, 2024 - Wiley Online Library
Abstract The Alzheimer's Disease Neuroimaging Initiative (ADNI) aims to improve
Alzheimer's disease (AD) clinical trials. Since 2006, ADNI has shared clinical, neuroimaging …
Alzheimer's disease (AD) clinical trials. Since 2006, ADNI has shared clinical, neuroimaging …