AIMC Topic: Brain

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Approximating anatomically-guided PET reconstruction in image space using a convolutional neural network.

NeuroImage
In the last two decades, it has been shown that anatomically-guided PET reconstruction can lead to improved bias-noise characteristics in brain PET imaging. However, despite promising results in simulations and first studies, anatomically-guided PET ...

Microscopic brain tumor detection and classification using 3D CNN and feature selection architecture.

Microscopy research and technique
Brain tumor is one of the most dreadful natures of cancer and caused a huge number of deaths among kids and adults from the past few years. According to WHO standard, the 700,000 humans are being with a brain tumor and around 86,000 are diagnosed sin...

A machine learning approach for magnetic resonance image-based mouse brain modeling and fast computation in controlled cortical impact.

Medical & biological engineering & computing
Computational modeling of the brain is crucial for the study of traumatic brain injury. An anatomically accurate model with refined details could provide the most accurate computational results. However, computational models with fine mesh details co...

Inner speech.

Wiley interdisciplinary reviews. Cognitive science
Inner speech travels under many aliases: the inner voice, verbal thought, thinking in words, internal verbalization, "talking in your head," the "little voice in the head," and so on. It is both a familiar element of first-person experience and a psy...

Differential Covariance: A New Method to Estimate Functional Connectivity in fMRI.

Neural computation
Measuring functional connectivity from fMRI recordings is important in understanding processing in cortical networks. However, because the brain's connection pattern is complex, currently used methods are prone to producing false functional connectio...

Updates on Deep Learning and Glioma: Use of Convolutional Neural Networks to Image Glioma Heterogeneity.

Neuroimaging clinics of North America
Deep learning represents end-to-end machine learning in which feature selection from images and classification happen concurrently. This articles provides updates on how deep learning is being applied to the study of glioma and its genetic heterogene...

Artificial Intelligence and Stroke Imaging: A West Coast Perspective.

Neuroimaging clinics of North America
Artificial intelligence (AI) advancements have significant implications for medical imaging. Stroke is the leading cause of disability and the fifth leading cause of death in the United States. AI applications for stroke imaging are a topic of intens...

Learning probabilistic neural representations with randomly connected circuits.

Proceedings of the National Academy of Sciences of the United States of America
The brain represents and reasons probabilistically about complex stimuli and motor actions using a noisy, spike-based neural code. A key building block for such neural computations, as well as the basis for supervised and unsupervised learning, is th...

GP-GAN: Brain tumor growth prediction using stacked 3D generative adversarial networks from longitudinal MR Images.

Neural networks : the official journal of the International Neural Network Society
Brain tumors are one of the major common causes of cancer-related death, worldwide. Growth prediction of these tumors, particularly gliomas which are the most dominant type, can be quite useful to improve treatment planning, quantify tumor aggressive...

Diverse Applications of Artificial Intelligence in Neuroradiology.

Neuroimaging clinics of North America
Recent advances in artificial intelligence (AI) and deep learning (DL) hold promise to augment neuroimaging diagnosis for patients with brain tumors and stroke. Here, the authors review the diverse landscape of emerging neuroimaging applications of A...