AIMC Topic: Brain

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Predicting sex from brain rhythms with deep learning.

Scientific reports
We have excellent skills to extract sex from visual assessment of human faces, but assessing sex from human brain rhythms seems impossible. Using deep convolutional neural networks, with unique potential to find subtle differences in apparent similar...

Concatenated and Connected Random Forests With Multiscale Patch Driven Active Contour Model for Automated Brain Tumor Segmentation of MR Images.

IEEE transactions on medical imaging
Segmentation of brain tumors from magnetic resonance imaging (MRI) data sets is of great importance for improved diagnosis, growth rate prediction, and treatment planning. However, automating this process is challenging due to the presence of severe ...

Deep learning enables reduced gadolinium dose for contrast-enhanced brain MRI.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: There are concerns over gadolinium deposition from gadolinium-based contrast agents (GBCA) administration.

3-D Fully Convolutional Networks for Multimodal Isointense Infant Brain Image Segmentation.

IEEE transactions on cybernetics
Accurate segmentation of infant brain images into different regions of interest is one of the most important fundamental steps in studying early brain development. In the isointense phase (approximately 6-8 months of age), white matter and gray matte...

Neuroanatomical morphometric characterization of sex differences in youth using statistical learning.

NeuroImage
Exploring neuroanatomical sex differences using a multivariate statistical learning approach can yield insights that cannot be derived with univariate analysis. While gross differences in total brain volume are well-established, uncovering the more s...

Ensemble support vector machine classification of dementia using structural MRI and mini-mental state examination.

Journal of neuroscience methods
BACKGROUND: The International Challenge for Automated Prediction of MCI from MRI data offered independent, standardized comparison of machine learning algorithms for multi-class classification of normal control (NC), mild cognitive impairment (MCI), ...

Anytime multipurpose emotion recognition from EEG data using a Liquid State Machine based framework.

Artificial intelligence in medicine
Recent technological advances in machine learning offer the possibility of decoding complex datasets and discern latent patterns. In this study, we adopt Liquid State Machines (LSM) to recognize the emotional state of an individual based on EEG data....

NiftyNet: a deep-learning platform for medical imaging.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVES: Medical image analysis and computer-assisted intervention problems are increasingly being addressed with deep-learning-based solutions. Established deep-learning platforms are flexible but do not provide specific functional...

Early prediction of cognitive deficits in very preterm infants using functional connectome data in an artificial neural network framework.

NeuroImage. Clinical
Investigation of the brain's functional connectome can improve our understanding of how an individual brain's organizational changes influence cognitive function and could result in improved individual risk stratification. Brain connectome studies in...