Advances in artificial intelligence have cultivated a strong interest in developing and
validating the clinical utilities of computer-aided diagnostic models. Machine learning for …

There are growing concerns about the generalizability of machine learning classifiers in
neuroimaging. In order to evaluate this aspect across relatively large heterogeneous …

OPINION ARTICLE published: 05 September 2012 doi: 10.3389/fnsys. 2012.00062 tarballs,
and via NITRC Image Repository (NITRC-IR3) which supports searches by phenotypic …

Neuroimaging is critical in clinical care and research, enabling us to investigate the brain in
health and disease. There is a complex link between the brain's morphological structure …

Abstract In the recent 5 years (2014–2018), there has been growing interest in the use of
machine learning (ML) techniques to explore image diagnosis and prognosis of therapeutic …

In an effort to promote the enhancement, adoption, distribution, and evolution of
neuroimaging informatics tools and resources, the National Institutes of Health (NIH) …

Conclusion The application of machine learning methods to neuroimaging has risen more
rapidly than could have been predicted 15 years ago. It has been a very exciting new …

In recent years, artificial intelligence (AI) or the study of how computers and machines can
gain intelligence, has been increasingly applied to problems in medical imaging, and in …

Abstract Machine learning is a powerful tool for creating computational models relating brain
function to behavior, and its use is becoming widespread in neuroscience. However, these …

Artificial intelligence (AI) has been successfully exploited in diagnosing many mental
disorders. Numerous systematic reviews summarize the evidence on the accuracy of AI …