AIMC Topic: Neural Networks, Computer

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Sensor-based machine learning for workflow detection and as key to detect expert level in laparoscopic suturing and knot-tying.

Surgical endoscopy
INTRODUCTION: The most common way of assessing surgical performance is by expert raters to view a surgical task and rate a trainee's performance. However, there is huge potential for automated skill assessment and workflow analysis using modern techn...

A collaborative neurodynamic approach to global and combinatorial optimization.

Neural networks : the official journal of the International Neural Network Society
In this paper, a collaborative neurodynamic optimization approach is proposed for global and combinatorial optimization. First, a combinatorial optimization problem is reformulated as a global optimization problem. Second, a neurodynamic optimization...

Contrastive Hebbian learning with random feedback weights.

Neural networks : the official journal of the International Neural Network Society
Neural networks are commonly trained to make predictions through learning algorithms. Contrastive Hebbian learning, which is a powerful rule inspired by gradient backpropagation, is based on Hebb's rule and the contrastive divergence algorithm. It op...

A deep learning approach for real-time detection of sleep spindles.

Journal of neural engineering
OBJECTIVE: Sleep spindles have been implicated in memory consolidation and synaptic plasticity during NREM sleep. Detection accuracy and latency in automatic spindle detection are critical for real-time applications.

3D convolutional neural networks for tumor segmentation using long-range 2D context.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
We present an efficient deep learning approach for the challenging task of tumor segmentation in multisequence MR images. In recent years, Convolutional Neural Networks (CNN) have achieved state-of-the-art performances in a large variety of recogniti...

Robust water-fat separation for multi-echo gradient-recalled echo sequence using convolutional neural network.

Magnetic resonance in medicine
PURPOSE: To accurately separate water and fat signals for bipolar multi-echo gradient-recalled echo sequence using a convolutional neural network (CNN).

Recognizing Physical Activity of Older People from Wearable Sensors and Inconsistent Data.

Sensors (Basel, Switzerland)
The physiological monitoring of older people using wearable sensors has shown great potential in improving their quality of life and preventing undesired events related to their health status. Nevertheless, creating robust predictive models from data...

Cell Identity Codes: Understanding Cell Identity from Gene Expression Profiles using Deep Neural Networks.

Scientific reports
Understanding cell identity is an important task in many biomedical areas. Expression patterns of specific marker genes have been used to characterize some limited cell types, but exclusive markers are not available for many cell types. A second appr...

Software Development Effort Estimation Using Regression Fuzzy Models.

Computational intelligence and neuroscience
Software effort estimation plays a critical role in project management. Erroneous results may lead to overestimating or underestimating effort, which can have catastrophic consequences on project resources. Machine-learning techniques are increasingl...

Multifocus Image Fusion Using Wavelet-Domain-Based Deep CNN.

Computational intelligence and neuroscience
Multifocus image fusion is the merging of images of the same scene and having multiple different foci into one all-focus image. Most existing fusion algorithms extract high-frequency information by designing local filters and then adopt different fus...