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Predicting Human Saccadic Scanpaths Based on Iterative Representation Learning.
IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Feb 7, 2019
Visual attention is a dynamic process of scene exploration and information acquisition. However, existing research on attention modeling has concentrated on estimating static salient locations. In contrast, dynamic attributes presented by saccade hav...
Semi-supervised learning to improve generalizability of risk prediction models.
Journal of biomedical informatics
Feb 7, 2019
The utility of a prediction model depends on its generalizability to patients drawn from different but related populations. We explored whether a semi-supervised learning model could improve the generalizability of colorectal cancer (CRC) risk predic...
Predicting centre of mass horizontal speed in low to severe swimming intensities with linear and non-linear models.
Journal of sports sciences
Feb 6, 2019
We aimed to compare multilayer perceptron (MLP) neural networks, radial basis function neural networks (RBF) and linear models (LM) accuracy to predict the centre of mass (CM) horizontal speed at low-moderate, heavy and severe swimming intensities us...
An integrated molecular diagnostic report for heart transplant biopsies using an ensemble of diagnostic algorithms.
The Journal of heart and lung transplantation : the official publication of the International Society for Heart Transplantation
Feb 6, 2019
BACKGROUND: We previously reported a microarray-based diagnostic system for heart transplant endomyocardial biopsies (EMBs), using either 3-archetype (3AA) or 4-archetype (4AA) unsupervised algorithms to estimate rejection. In the present study we ex...
Sprint Assessment Using Machine Learning and a Wearable Accelerometer.
Journal of applied biomechanics
Feb 5, 2019
Field-based sprint performance assessments rely on metrics derived from a simple model of sprinting dynamics parameterized by 2 constants, v and τ, which indicate a sprinter's maximal theoretical velocity and the time it takes to approach v, respecti...
Using computer-vision and machine learning to automate facial coding of positive and negative affect intensity.
PloS one
Feb 5, 2019
Facial expressions are fundamental to interpersonal communication, including social interaction, and allow people of different ages, cultures, and languages to quickly and reliably convey emotional information. Historically, facial expression researc...
Evaluating sociotechnical dynamics in a simulated remotely-piloted aircraft system: a layered dynamics approach.
Ergonomics
Feb 4, 2019
As coordination mechanisms change and technology failures occur, a sociotechnical system must reorganise itself across human and technological layers to maintain effectiveness. We present a study examining reorganisation across communication, control...
Radiomics-based machine learning methods for isocitrate dehydrogenase genotype prediction of diffuse gliomas.
Journal of cancer research and clinical oncology
Feb 4, 2019
PURPOSE: Reliable and accurate predictive models are necessary to drive the success of radiomics. Our aim was to identify the optimal radiomics-based machine learning method for isocitrate dehydrogenase (IDH) genotype prediction in diffuse gliomas.
Automated selection of myocardial inversion time with a convolutional neural network: Spatial temporal ensemble myocardium inversion network (STEMI-NET).
Magnetic resonance in medicine
Feb 3, 2019
PURPOSE: Delayed enhancement imaging is an essential component of cardiac MRI, which is used widely for the evaluation of myocardial scar and viability. The selection of an optimal inversion time (TI) or null point (TI ) to suppress the background my...
Automatic Breast and Fibroglandular Tissue Segmentation in Breast MRI Using Deep Learning by a Fully-Convolutional Residual Neural Network U-Net.
Academic radiology
Jan 31, 2019
RATIONALE AND OBJECTIVES: Breast segmentation using the U-net architecture was implemented and tested in independent validation datasets to quantify fibroglandular tissue volume in breast MRI.
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