AIMC Topic: Reproducibility of Results

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Spectral and Temporal Feature Learning With Two-Stream Neural Networks for Mental Workload Assessment.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
People's mental workload profoundly affects their work efficiency and health. Mental workload assessment can be used to effectively avoid serious accidents caused by excessive mental workload. Both electroencephalogram (EEG) spectral features and its...

Preoperative evaluation of tumour consistency in pituitary macroadenomas: a machine learning-based histogram analysis on conventional T2-weighted MRI.

Neuroradiology
PURPOSE: To evaluate the potential value of machine learning (ML)-based histogram analysis (or first-order texture analysis) on T2-weighted magnetic resonance imaging (MRI) for predicting consistency of pituitary macroadenomas (PMA) and to compare it...

mACPpred: A Support Vector Machine-Based Meta-Predictor for Identification of Anticancer Peptides.

International journal of molecular sciences
Anticancer peptides (ACPs) are promising therapeutic agents for targeting and killing cancer cells. The accurate prediction of ACPs from given peptide sequences remains as an open problem in the field of immunoinformatics. Recently, machine learning ...

Advanced atherosclerosis imaging by CT: Radiomics, machine learning and deep learning.

Journal of cardiovascular computed tomography
In the last decade, technical advances in the field of medical imaging significantly improved and broadened the application of coronary CT angiography (CCTA) for the non-invasive assessment of coronary artery disease. Recently, similar breakthroughs ...

The Four Horsemen of the 'Omicsalypse': ontology, replicability, probability and epistemology.

Human genetics
Much of modern genomics and the other 'omics' that tag along, assert that the causal bases of biomedical outcomes are genomically enumerable lists whose effects are predictable with 'precision', extensible from samples to all, and enabled by ever-gre...

Machine learning methods for automated technical skills assessment with instructional feedback in ultrasound-guided interventions.

International journal of computer assisted radiology and surgery
OBJECTIVE: Currently, there is a worldwide shift toward competency-based medical education. This necessitates the use of automated skills assessment methods during self-guided interventions training. Making assessment methods that are transparent and...

Automated and accurate quantification of subcutaneous and visceral adipose tissue from magnetic resonance imaging based on machine learning.

Magnetic resonance imaging
Accurate measuring of subcutaneous adipose tissue (SAT) and visceral adipose tissue (VAT) is vital for the research of many diseases. The localization and quantification of SAT and VAT by computed tomography (CT) expose patients to harmful ionizing r...

Diagnosis of pain in the right iliac fossa. A new diagnostic score based on Decision-Tree and Artificial Neural Network Methods.

Cirugia espanola
INTRODUCTION: Pain in the right iliac fossa (RIF) continues to pose diagnostic challenges. The objective of this study is the development of a RIF pain diagnosis model based on classification trees of type CHAID (Chi-Square Automatic Interaction Dete...

Photomontage detection using steganography technique based on a neural network.

Neural networks : the official journal of the International Neural Network Society
This article presents a steganographic method StegoNN based on neural networks. The method is able to identify a photomontage from presented signed images. Unlike other academic approaches using neural networks primarily as classifiers, the StegoNN m...