eXplainable Artificial Intelligence (XAI) in aging clock models
XAI is a rapidly progressing field of machine learning, aiming to unravel the predictions of
complex models. XAI is especially required in sensitive applications, eg in health care, when …
complex models. XAI is especially required in sensitive applications, eg in health care, when …
CReg-KD: Model refinement via confidence regularized knowledge distillation for brain imaging
One of the core challenges of deep learning in medical image analysis is data insufficiency,
especially for 3D brain imaging, which may lead to model over-fitting and poor …
especially for 3D brain imaging, which may lead to model over-fitting and poor …
DDParcel: deep learning anatomical brain parcellation from diffusion MRI
Parcellation of anatomically segregated cortical and subcortical brain regions is required in
diffusion MRI (dMRI) analysis for region-specific quantification and better anatomical …
diffusion MRI (dMRI) analysis for region-specific quantification and better anatomical …
[HTML][HTML] Advanced brain age correlates with greater rumination and less mindfulness in schizophrenia
Background Individual variation in brain aging trajectories is linked with several physical
and mental health outcomes. Greater stress levels, worry, and rumination correspond with …
and mental health outcomes. Greater stress levels, worry, and rumination correspond with …
eXplainable Artificial Intelligence (XAI) in age prediction: A systematic review
A Kalyakulina, I Yusipov - arXiv preprint arXiv:2307.13704, 2023 - arxiv.org
eXplainable Artificial Intelligence (XAI) is now an important and essential part of machine
learning, allowing to explain the predictions of complex models. XAI is especially required in …
learning, allowing to explain the predictions of complex models. XAI is especially required in …
EEGProgress: A fast and lightweight progressive convolution architecture for EEG classification
Because of the intricate topological structure and connection of the human brain, extracting
deep spatial features from electroencephalograph (EEG) signals is a challenging and time …
deep spatial features from electroencephalograph (EEG) signals is a challenging and time …
Magnetic resonance image-based brain age as a discriminator of dementia conversion in patients with amyloid-negative amnestic mild cognitive impairment
HW Kim, HJ Kim, H Lee, H Yang, ZH Rieu, JH Lee - Scientific Reports, 2023 - nature.com
Patients with amyloid-negative amnestic mild cognitive impairment (MCI) have a conversion
rate of approximately 10% to dementia within 2 years. We aimed to investigate whether …
rate of approximately 10% to dementia within 2 years. We aimed to investigate whether …
Age Prediction Using Resting-State Functional MRI
The increasing lifespan and large individual differences in cognitive capability highlight the
importance of comprehending the aging process of the brain. Contrary to visible signs of …
importance of comprehending the aging process of the brain. Contrary to visible signs of …
Deep interpretability methods for neuroimaging
MM Rahman - 2022 - scholarworks.gsu.edu
Brain dynamics are highly complex and yet hold the key to understanding brain function and
dysfunction. The dynamics captured by resting-state functional magnetic resonance imaging …
dysfunction. The dynamics captured by resting-state functional magnetic resonance imaging …
Disentangling neurodegeneration from ageing in multiple sclerosis: the brain-predicted disease duration gap
Disentangling brain ageing from disease-related neurodegeneration in patients with
multiple sclerosis (PwMS) is increasingly topical. The brain-age paradigm offers a window …
multiple sclerosis (PwMS) is increasingly topical. The brain-age paradigm offers a window …