Machine learning methods hold the potential of having a significant and profound impact on neuroimaging analysis and treatment, therapeutic decisions and may ultimately improve the outcome for patients. Advances in network design, processing power, the availability of easy-to-use software packages, and the scale of available medical image databases have accelerated the developments in this exciting field. Nevertheless, studies evaluating the potential applications of machine learning methods for detection, lesion segmentation, therapeutic decision, and prognosis of brain disease are still relatively sparse.
The aim of this Research Topic is to cover novel and promising research trends in applying machine learning to the evaluation of images of the brain. This Research Topic focuses on recent advances in the applications of machine learning and, in particular, the applications of deep learning to neuroimaging.
For this purpose, we welcome high quality original research or review articles relating to the application of current machine learning methods to neuroimaging including; clinical applications, methods, data augmentation, machine learning interpretation, and new algorithm design.
Potential areas of interest include, but are not limited to:
- Machine Learning for detection or segmentation of brain pathologies in various imaging modalities including MRI, CT, PET, SPECT and NMR
- Machine Learning of neuroimaging biomarkers
- Predictive modelling of treatment efficacy
- Decision support algorithms
- Multi-parametric studies including clinical, epidemiological, biochemical information to neuroimaging information
Machine learning methods hold the potential of having a significant and profound impact on neuroimaging analysis and treatment, therapeutic decisions and may ultimately improve the outcome for patients. Advances in network design, processing power, the availability of easy-to-use software packages, and the scale of available medical image databases have accelerated the developments in this exciting field. Nevertheless, studies evaluating the potential applications of machine learning methods for detection, lesion segmentation, therapeutic decision, and prognosis of brain disease are still relatively sparse.
The aim of this Research Topic is to cover novel and promising research trends in applying machine learning to the evaluation of images of the brain. This Research Topic focuses on recent advances in the applications of machine learning and, in particular, the applications of deep learning to neuroimaging.
For this purpose, we welcome high quality original research or review articles relating to the application of current machine learning methods to neuroimaging including; clinical applications, methods, data augmentation, machine learning interpretation, and new algorithm design.
Potential areas of interest include, but are not limited to:
- Machine Learning for detection or segmentation of brain pathologies in various imaging modalities including MRI, CT, PET, SPECT and NMR
- Machine Learning of neuroimaging biomarkers
- Predictive modelling of treatment efficacy
- Decision support algorithms
- Multi-parametric studies including clinical, epidemiological, biochemical information to neuroimaging information
