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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 …
Quantitative approaches to guide epilepsy surgery from intracranial EEG
Over the past 10 years, the drive to improve outcomes from epilepsy surgery has stimulated
widespread interest in methods to quantitatively guide epilepsy surgery from intracranial …
widespread interest in methods to quantitatively guide epilepsy surgery from intracranial …
Data leakage inflates prediction performance in connectome-based machine learning models
Predictive modeling is a central technique in neuroimaging to identify brain-behavior
relationships and test their generalizability to unseen data. However, data leakage …
relationships and test their generalizability to unseen data. However, data leakage …
[HTML][HTML] Class imbalance should not throw you off balance: Choosing the right classifiers and performance metrics for brain decoding with imbalanced data
Abstract Machine learning (ML) is increasingly used in cognitive, computational and clinical
neuroscience. The reliable and efficient application of ML requires a sound understanding of …
neuroscience. The reliable and efficient application of ML requires a sound understanding of …
How to avoid machine learning pitfalls: a guide for academic researchers
MA Lones - arxiv preprint arxiv:2108.02497, 2021 - arxiv.org
Mistakes in machine learning practice are commonplace, and can result in a loss of
confidence in the findings and products of machine learning. This guide outlines common …
confidence in the findings and products of machine learning. This guide outlines common …
A guided multiverse study of neuroimaging analyses
For most neuroimaging questions the range of possible analytic choices makes it unclear
how to evaluate conclusions from any single analytic method. One possible way to address …
how to evaluate conclusions from any single analytic method. One possible way to address …
The performance of machine learning models in predicting suicidal ideation, attempts, and deaths: a meta-analysis and systematic review
Research has posited that machine learning could improve suicide risk prediction models,
which have traditionally performed poorly. This systematic review and meta-analysis …
which have traditionally performed poorly. This systematic review and meta-analysis …
Cross-validation strategy impacts the performance and interpretation of machine learning models
Abstract Machine learning algorithms are able to capture complex, nonlinear, interacting
relationships and are increasingly used to predict agricultural yield variability at regional and …
relationships and are increasingly used to predict agricultural yield variability at regional and …
Predicting ACL injury using machine learning on data from an extensive screening test battery of 880 female elite athletes
Background: Injury risk prediction is an emerging field in which more research is needed to
recognize the best practices for accurate injury risk assessment. Important issues related to …
recognize the best practices for accurate injury risk assessment. Important issues related to …
Dermal features derived from optoacoustic tomograms via machine learning correlate microangiopathy phenotypes with diabetes stage
Skin microangiopathy has been associated with diabetes. Here we show that skin-
microangiopathy phenotypes in humans can be correlated with diabetes stage via …
microangiopathy phenotypes in humans can be correlated with diabetes stage via …