AIMC Topic: Machine Learning

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Keep it real: rethinking the primacy of experimental control in cognitive neuroscience.

NeuroImage
Naturalistic experimental paradigms in neuroimaging arose from a pressure to test the validity of models we derive from highly-controlled experiments in real-world contexts. In many cases, however, such efforts led to the realization that models deve...

Structural characterization of the Extended Frontal Aslant Tract trajectory: A ML-validated laterality study in 3T and 7T.

NeuroImage
The Extended Frontal Aslant Tract (exFAT) is a recently described tractography-based extension of the Frontal Aslant Tract connecting Broca's territory to both supplementary and pre-supplementary motor areas, and more anterior prefrontal regions. In ...

Deep neural networks enable quantitative movement analysis using single-camera videos.

Nature communications
Many neurological and musculoskeletal diseases impair movement, which limits people's function and social participation. Quantitative assessment of motion is critical to medical decision-making but is currently possible only with expensive motion cap...

Diagnosis of diabetes in pregnant woman using a Chaotic-Jaya hybridized extreme learning machine model.

Journal of integrative bioinformatics
As stated by World Health Organization (WHO) report, 246 million individuals have suffered with diabetes disease over worldwide and it is anticipated that by 2025 this estimation can cross 380 million. So, the proper and quick diagnosis of this disea...

A machine learning-based approach for estimating and testing associations with multivariate outcomes.

The international journal of biostatistics
We propose a method for summarizing the strength of association between a set of variables and a multivariate outcome. Classical summary measures are appropriate when linear relationships exist between covariates and outcomes, while our approach prov...

Evolution and impact of bias in human and machine learning algorithm interaction.

PloS one
Traditionally, machine learning algorithms relied on reliable labels from experts to build predictions. More recently however, algorithms have been receiving data from the general population in the form of labeling, annotations, etc. The result is th...

Machine Learning in Radiomic Renal Mass Characterization: Fundamentals, Applications, Challenges, and Future Directions.

AJR. American journal of roentgenology
The purpose of this study is to provide an overview of the traditional machine learning (ML)-based and deep learning-based radiomic approaches, with focus placed on renal mass characterization. ML currently has a very low barrier to entry into gene...

Deep Learning Driven GC-MS Library Search and Its Application for Metabolomics.

Analytical chemistry
Preliminary compound identification and peak annotation in gas chromatography-mass spectrometry is usually made using mass spectral databases. There are a few algorithms that enable performing a search of a spectrum in a large mass spectral library. ...

Morphological Characterization of Functional Brain Imaging by Isosurface Analysis in Parkinson's Disease.

International journal of neural systems
Finding new biomarkers to model Parkinson's Disease (PD) is a challenge not only to help discerning between Healthy Control (HC) subjects and patients with potential PD but also as a way to measure quantitatively the loss of dopaminergic neurons main...

Functional cardiac CT-Going beyond Anatomical Evaluation of Coronary Artery Disease with Cine CT, CT-FFR, CT Perfusion and Machine Learning.

The British journal of radiology
The aim of this review is to provide an overview of different functional cardiac CT techniques which can be used to supplement assessment of the coronary arteries to establish the significance of coronary artery stenoses. We focus on cine-CT, CT-FFR,...