Machine learning for medical imaging: methodological failures and recommendations for the future
Research in computer analysis of medical images bears many promises to improve patients'
health. However, a number of systematic challenges are slowing down the progress of the …
health. However, a number of systematic challenges are slowing down the progress of the …
Digital medicine and the curse of dimensionality
Digital health data are multimodal and high-dimensional. A patient's health state can be
characterized by a multitude of signals including medical imaging, clinical variables …
characterized by a multitude of signals including medical imaging, clinical variables …
Machine learning algorithm validation with a limited sample size
Advances in neuroimaging, genomic, motion tracking, eye-tracking and many other
technology-based data collection methods have led to a torrent of high dimensional …
technology-based data collection methods have led to a torrent of high dimensional …
Machine learning methods for predicting progression from mild cognitive impairment to Alzheimer's disease dementia: a systematic review
S Grueso, R Viejo-Sobera - Alzheimer's research & therapy, 2021 - Springer
Background An increase in lifespan in our society is a double-edged sword that entails a
growing number of patients with neurocognitive disorders, Alzheimer's disease being the …
growing number of patients with neurocognitive disorders, Alzheimer's disease being the …
Different scaling of linear models and deep learning in UKBiobank brain images versus machine-learning datasets
Recently, deep learning has unlocked unprecedented success in various domains,
especially using images, text, and speech. However, deep learning is only beneficial if the …
especially using images, text, and speech. However, deep learning is only beneficial if the …
Ten Years of BrainAGE as a Neuroimaging Biomarker of Brain Aging: What Insights Have We Gained?
With the aging population, prevalence of neurodegenerative diseases is increasing, thus
placing a growing burden on individuals and the whole society. However, individual rates of …
placing a growing burden on individuals and the whole society. However, individual rates of …
Machine learning in mental health: A systematic review of the HCI literature to support the development of effective and implementable ML systems
High prevalence of mental illness and the need for effective mental health care, combined
with recent advances in AI, has led to an increase in explorations of how the field of machine …
with recent advances in AI, has led to an increase in explorations of how the field of machine …
Clinical prediction models in psychiatry: a systematic review of two decades of progress and challenges
Recent years have seen the rapid proliferation of clinical prediction models aiming to
support risk stratification and individualized care within psychiatry. Despite growing interest …
support risk stratification and individualized care within psychiatry. Despite growing interest …
Hierarchical fully convolutional network for joint atrophy localization and Alzheimer's disease diagnosis using structural MRI
Structural magnetic resonance imaging (sMRI) has been widely used for computer-aided
diagnosis of neurodegenerative disorders, eg, Alzheimer's disease (AD), due to its …
diagnosis of neurodegenerative disorders, eg, Alzheimer's disease (AD), due to its …
[HTML][HTML] Identification of autism spectrum disorder using deep learning and the ABIDE dataset
The goal of the present study was to apply deep learning algorithms to identify autism
spectrum disorder (ASD) patients from large brain imaging dataset, based solely on the …
spectrum disorder (ASD) patients from large brain imaging dataset, based solely on the …