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Deep image reconstruction from human brain activity.

PLoS computational biology
The mental contents of perception and imagery are thought to be encoded in hierarchical representations in the brain, but previous attempts to visualize perceptual contents have failed to capitalize on multiple levels of the hierarchy, leaving it cha...

Semi-supervised encoding for outlier detection in clinical observation data.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Electronic Health Record (EHR) data often include observation records that are unlikely to represent the "truth" about a patient at a given clinical encounter. Due to their high throughput, examples of such implausible obser...

Training recurrent neural networks robust to incomplete data: Application to Alzheimer's disease progression modeling.

Medical image analysis
Disease progression modeling (DPM) using longitudinal data is a challenging machine learning task. Existing DPM algorithms neglect temporal dependencies among measurements, make parametric assumptions about biomarker trajectories, do not model multip...

Automated identification for autism severity level: EEG analysis using empirical mode decomposition and second order difference plot.

Behavioural brain research
BACKGROUND: Previous automated EEG-based diagnosis of autism spectrum disorders (ASD) using various nonlinear EEG analysis methods were limited to distinguish only children with ASD from those normally developed without approaching their autistic fea...

The effect of pasture quantity temporal variation on milking robot utilization.

Journal of dairy science
In pasture-based automatic milking systems (AMS), a decrease in robot utilization (RU) often occurs in the early morning hours. Novel feeding strategies that encourage voluntary cow traffic throughout 24 h could help mitigate this problem. We determi...

Using neuroimaging to predict relapse in stimulant dependence: A comparison of linear and machine learning models.

NeuroImage. Clinical
OBJECTIVE: Relapse rates are consistently high for stimulant user disorders. In order to obtain prognostic information about individuals in treatment, machine learning models have been applied to neuroimaging and clinical data. Yet few efforts have b...

EEG-Based Spatio-Temporal Convolutional Neural Network for Driver Fatigue Evaluation.

IEEE transactions on neural networks and learning systems
Driver fatigue evaluation is of great importance for traffic safety and many intricate factors would exacerbate the difficulty. In this paper, based on the spatial-temporal structure of multichannel electroencephalogram (EEG) signals, we develop a no...

Improving the Quality of Synthetic FLAIR Images with Deep Learning Using a Conditional Generative Adversarial Network for Pixel-by-Pixel Image Translation.

AJNR. American journal of neuroradiology
BACKGROUND AND PURPOSE: Synthetic FLAIR images are of lower quality than conventional FLAIR images. Here, we aimed to improve the synthetic FLAIR image quality using deep learning with pixel-by-pixel translation through conditional generative adversa...

Computational prediction of diagnosis and feature selection on mesothelioma patient health records.

PloS one
BACKGROUND: Mesothelioma is a lung cancer that kills thousands of people worldwide annually, especially those with exposure to asbestos. Diagnosis of mesothelioma in patients often requires time-consuming imaging techniques and biopsies. Machine lear...