AIMC Topic: Neuroimaging

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Random support vector machine cluster analysis of resting-state fMRI in Alzheimer's disease.

PloS one
Early diagnosis is critical for individuals with Alzheimer's disease (AD) in clinical practice because its progress is irreversible. In the existing literature, support vector machine (SVM) has always been applied to distinguish between AD and health...

Automatically measuring brain ventricular volume within PACS using artificial intelligence.

PloS one
The picture archiving and communications system (PACS) is currently the standard platform to manage medical images but lacks analytical capabilities. Staying within PACS, the authors have developed an automatic method to retrieve the medical data and...

Investigating brain structural patterns in first episode psychosis and schizophrenia using MRI and a machine learning approach.

Psychiatry research. Neuroimaging
In this study, we employed the Maximum Uncertainty Linear Discriminant Analysis (MLDA) to investigate whether the structural brain patterns in first episode psychosis (FEP) patients would be more similar to patients with chronic schizophrenia (SCZ) o...

Fully-automated alignment of 3D fetal brain ultrasound to a canonical reference space using multi-task learning.

Medical image analysis
Methods for aligning 3D fetal neurosonography images must be robust to (i) intensity variations, (ii) anatomical and age-specific differences within the fetal population, and (iii) the variations in fetal position. To this end, we propose a multi-tas...

Deep Learning in Neuroradiology.

AJNR. American journal of neuroradiology
Deep learning is a form of machine learning using a convolutional neural network architecture that shows tremendous promise for imaging applications. It is increasingly being adapted from its original demonstration in computer vision applications to ...

Semi-Supervised Discriminative Classification Robust to Sample-Outliers and Feature-Noises.

IEEE transactions on pattern analysis and machine intelligence
Discriminative methods commonly produce models with relatively good generalization abilities. However, this advantage is challenged in real-world applications (e.g., medical image analysis problems), in which there often exist outlier data points (sa...

The same analysis approach: Practical protection against the pitfalls of novel neuroimaging analysis methods.

NeuroImage
Standard neuroimaging data analysis based on traditional principles of experimental design, modelling, and statistical inference is increasingly complemented by novel analysis methods, driven e.g. by machine learning methods. While these novel approa...

Using diffusion MRI to discriminate areas of cortical grey matter.

NeuroImage
Cortical area parcellation is a challenging problem that is often approached by combining structural imaging (e.g., quantitative T1, diffusion-based connectivity) with functional imaging (e.g., task activations, topological mapping, resting state cor...

Image processing and Quality Control for the first 10,000 brain imaging datasets from UK Biobank.

NeuroImage
UK Biobank is a large-scale prospective epidemiological study with all data accessible to researchers worldwide. It is currently in the process of bringing back 100,000 of the original participants for brain, heart and body MRI, carotid ultrasound an...