AIMC Topic: Brain

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EnvCNN: A Convolutional Neural Network Model for Evaluating Isotopic Envelopes in Top-Down Mass-Spectral Deconvolution.

Analytical chemistry
Top-down mass spectrometry has become the main method for intact proteoform identification, characterization, and quantitation. Because of the complexity of top-down mass spectrometry data, spectral deconvolution is an indispensable step in spectral ...

An unsupervised deep learning technique for susceptibility artifact correction in reversed phase-encoding EPI images.

Magnetic resonance imaging
Echo planar imaging (EPI) is a fast and non-invasive magnetic resonance imaging technique that supports data acquisition at high spatial and temporal resolutions. However, susceptibility artifacts, which cause the misalignment to the underlying struc...

Sparsity through evolutionary pruning prevents neuronal networks from overfitting.

Neural networks : the official journal of the International Neural Network Society
Modern Machine learning techniques take advantage of the exponentially rising calculation power in new generation processor units. Thus, the number of parameters which are trained to solve complex tasks was highly increased over the last decades. How...

Proposing a convolutional neural network for stress assessment by means of derived heart rate from functional near infrared spectroscopy.

Computers in biology and medicine
BACKGROUND: Stress is known as one of the major factors threatening human health. A large number of studies have been performed in order to either assess or relieve stress by analyzing the brain and heart-related signals.

Towards a brain-based predictome of mental illness.

Human brain mapping
Neuroimaging-based approaches have been extensively applied to study mental illness in recent years and have deepened our understanding of both cognitively healthy and disordered brain structure and function. Recent advancements in machine learning t...

Fully automated brain resection cavity delineation for radiation target volume definition in glioblastoma patients using deep learning.

Radiation oncology (London, England)
BACKGROUND: Automated brain tumor segmentation methods are computational algorithms that yield tumor delineation from, in this case, multimodal magnetic resonance imaging (MRI). We present an automated segmentation method and its results for resectio...

Deep learning based mild cognitive impairment diagnosis using structure MR images.

Neuroscience letters
Mild cognitive impairment (MCI) is an early sign of Alzheimer's disease (AD) which is the fourth leading disease mostly found in the aged population. Early intervention of MCI will possibly delay the progress towards AD, and this makes it very import...

The NanoZoomer artificial intelligence connectomics pipeline for tracer injection studies of the marmoset brain.

Brain structure & function
We describe our connectomics pipeline for processing anterograde tracer injection data for the brain of the common marmoset (Callithrix jacchus). Brain sections were imaged using a batch slide scanner (NanoZoomer 2.0-HT) and we used artificial intell...

The reliability of a deep learning model in clinical out-of-distribution MRI data: A multicohort study.

Medical image analysis
Deep learning (DL) methods have in recent years yielded impressive results in medical imaging, with the potential to function as clinical aid to radiologists. However, DL models in medical imaging are often trained on public research cohorts with ima...