AIMC Topic: Magnetic Resonance Imaging

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Metric learning with spectral graph convolutions on brain connectivity networks.

NeuroImage
Graph representations are often used to model structured data at an individual or population level and have numerous applications in pattern recognition problems. In the field of neuroscience, where such representations are commonly used to model str...

Can we accurately classify schizophrenia patients from healthy controls using magnetic resonance imaging and machine learning? A multi-method and multi-dataset study.

Schizophrenia research
Machine learning is a powerful tool that has previously been used to classify schizophrenia (SZ) patients from healthy controls (HC) using magnetic resonance images. Each study, however, uses different datasets, classification algorithms, and validat...

Neuroanatomical heterogeneity of schizophrenia revealed by semi-supervised machine learning methods.

Schizophrenia research
UNLABELLED: Schizophrenia is associated with heterogeneous clinical symptoms and neuroanatomical alterations. In this work, we aim to disentangle the patterns of neuroanatomical alterations underlying a heterogeneous population of patients using a se...

Subtyping cognitive profiles in Autism Spectrum Disorder using a Functional Random Forest algorithm.

NeuroImage
DSM-5 Autism Spectrum Disorder (ASD) comprises a set of neurodevelopmental disorders characterized by deficits in social communication and interaction and repetitive behaviors or restricted interests, and may both affect and be affected by multiple c...

MRI radiomics analysis of molecular alterations in low-grade gliomas.

International journal of computer assisted radiology and surgery
PURPOSE: Low-grade gliomas (LGG) are classified into three distinct groups based on their IDH1 mutation and 1p/19q codeletion status, each of which is associated with a different clinical expression. The genomic sub-classification of LGG requires tum...

Estimation of the Volume of the Left Ventricle From MRI Images Using Deep Neural Networks.

IEEE transactions on cybernetics
Segmenting human left ventricle (LV) in magnetic resonance imaging images and calculating its volume are important for diagnosing cardiac diseases. The latter task became the topic of the Second Annual Data Science Bowl organized by Kaggle. The datas...

Enhancing interpretability of automatically extracted machine learning features: application to a RBM-Random Forest system on brain lesion segmentation.

Medical image analysis
Machine learning systems are achieving better performances at the cost of becoming increasingly complex. However, because of that, they become less interpretable, which may cause some distrust by the end-user of the system. This is especially importa...

White matter hyperintensity and stroke lesion segmentation and differentiation using convolutional neural networks.

NeuroImage. Clinical
White matter hyperintensities (WMH) are a feature of sporadic small vessel disease also frequently observed in magnetic resonance images (MRI) of healthy elderly subjects. The accurate assessment of WMH burden is of crucial importance for epidemiolog...