AIMC Topic: Brain

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A Comparison of Three Different Deep Learning-Based Models to Predict the MGMT Promoter Methylation Status in Glioblastoma Using Brain MRI.

Journal of digital imaging
Glioblastoma (GBM) is the most common primary malignant brain tumor in adults. The standard treatment for GBM consists of surgical resection followed by concurrent chemoradiotherapy and adjuvant temozolomide. O-6-methylguanine-DNA methyltransferase (...

Deep Learning With Radiogenomics Towards Personalized Management of Gliomas.

IEEE reviews in biomedical engineering
A state-of-the-art interdisciplinary survey on multi-modal radiogenomic approaches is presented involving applications to the diagnosis and personalized management of gliomas a common kind of brain tumors through noninvasive imaging integrated with g...

Anatomically interpretable deep learning of brain age captures domain-specific cognitive impairment.

Proceedings of the National Academy of Sciences of the United States of America
The gap between chronological age (CA) and biological brain age, as estimated from magnetic resonance images (MRIs), reflects how individual patterns of neuroanatomic aging deviate from their typical trajectories. MRI-derived brain age (BA) estimates...

Computational models of Idling brain activity for memory processing.

Neuroscience research
Studying the underlying neural mechanisms of cognitive functions of the brain is one of the central questions in modern biology. Moreover, it has significantly impacted the development of novel technologies in artificial intelligence. Spontaneous act...

3D Soma Detection in Large-Scale Whole Brain Images via a Two-Stage Neural Network.

IEEE transactions on medical imaging
3D soma detection in whole brain images is a critical step for neuron reconstruction. However, existing soma detection methods are not suitable for whole mouse brain images with large amounts of data and complex structure. In this paper, we propose a...

Multi-task deep learning for medical image computing and analysis: A review.

Computers in biology and medicine
The renaissance of deep learning has provided promising solutions to various tasks. While conventional deep learning models are constructed for a single specific task, multi-task deep learning (MTDL) that is capable to simultaneously accomplish at le...

Deep Learning for Brain MRI Confirms Patterned Pathological Progression in Alzheimer's Disease.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Deep learning (DL) on brain magnetic resonance imaging (MRI) data has shown excellent performance in differentiating individuals with Alzheimer's disease (AD). However, the value of DL in detecting progressive structural MRI (sMRI) abnormalities link...

Early Diagnosis of Brain Diseases Using Artificial Intelligence and EV Molecular Data: A Proposed Noninvasive Repeated Diagnosis Approach.

Cells
Brain-derived extracellular vesicles (BDEVs) are released from the central nervous system. Brain-related research and diagnostic techniques involving BDEVs have rapidly emerged as a means of diagnosing brain disorders because they are minimally invas...

Reinforcement learning using Deep networks and learning accurately localizes brain tumors on MRI with very small training sets.

BMC medical imaging
BACKGROUND: Supervised deep learning in radiology suffers from notorious inherent limitations: 1) It requires large, hand-annotated data sets; (2) It is non-generalizable; and (3) It lacks explainability and intuition. It has recently been proposed t...