AIMC Topic: Brain

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I tried a bunch of things: The dangers of unexpected overfitting in classification of brain data.

Neuroscience and biobehavioral reviews
Machine learning has enhanced the abilities of neuroscientists to interpret information collected through EEG, fMRI, and MEG data. With these powerful techniques comes the danger of overfitting of hyperparameters which can render results invalid. We ...

Identifying and validating subtypes within major psychiatric disorders based on frontal-posterior functional imbalance via deep learning.

Molecular psychiatry
Converging evidence increasingly implicates shared etiologic and pathophysiological characteristics among major psychiatric disorders (MPDs), such as schizophrenia (SZ), bipolar disorder (BD), and major depressive disorder (MDD). Examining the neurob...

Difficulty-aware hierarchical convolutional neural networks for deformable registration of brain MR images.

Medical image analysis
The aim of deformable brain image registration is to align anatomical structures, which can potentially vary with large and complex deformations. Anatomical structures vary in size and shape, requiring the registration algorithm to estimate deformati...

Accelerating T mapping of the brain by integrating deep learning priors with low-rank and sparse modeling.

Magnetic resonance in medicine
PURPOSE: To accelerate T mapping with highly sparse sampling by integrating deep learning image priors with low-rank and sparse modeling.

Placing language in an integrated understanding system: Next steps toward human-level performance in neural language models.

Proceedings of the National Academy of Sciences of the United States of America
Language is crucial for human intelligence, but what exactly is its role? We take language to be a part of a system for understanding and communicating about situations. In humans, these abilities emerge gradually from experience and depend on domain...

3D Deep Neural Network Segmentation of Intracerebral Hemorrhage: Development and Validation for Clinical Trials.

Neuroinformatics
Intracranial hemorrhage (ICH) occurs when a blood vessel ruptures in the brain. This leads to significant morbidity and mortality, the likelihood of which is predicated on the size of the bleeding event. X-ray computed tomography (CT) scans allow cli...

High-performance rapid MR parameter mapping using model-based deep adversarial learning.

Magnetic resonance imaging
PURPOSE: To develop and evaluate a deep adversarial learning-based image reconstruction approach for rapid and efficient MR parameter mapping.

Learning patterns of the ageing brain in MRI using deep convolutional networks.

NeuroImage
Both normal ageing and neurodegenerative diseases cause morphological changes to the brain. Age-related brain changes are subtle, nonlinear, and spatially and temporally heterogenous, both within a subject and across a population. Machine learning mo...

Deep learning-based synthetic CT generation for paediatric brain MR-only photon and proton radiotherapy.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
BACKGROUND AND PURPOSE: To enable accurate magnetic resonance imaging (MRI)-based dose calculations, synthetic computed tomography (sCT) images need to be generated. We aim at assessing the feasibility of dose calculations from MRI acquired with a he...

Automatic Prediction of MGMT Status in Glioblastoma via Deep Learning-Based MR Image Analysis.

BioMed research international
Methylation of the O-methylguanine methyltransferase (MGMT) gene promoter is correlated with the effectiveness of the current standard of care in glioblastoma patients. In this study, a deep learning pipeline is designed for automatic prediction of M...