AIMC Topic: Neural Networks, Computer

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A small-world topology enhances the echo state property and signal propagation in reservoir computing.

Neural networks : the official journal of the International Neural Network Society
Cortical neural connectivity has been shown to exhibit a small-world (SW) network topology. However, the role of the topology in neural information processing remains unclear. In this study, we investigated the learning performance of an echo state n...

Application of supervised machine learning algorithms in the classification of sagittal gait patterns of cerebral palsy children with spastic diplegia.

Computers in biology and medicine
Gait classification has been widely used for children with cerebral palsy (CP) to assist with clinical decision making and to evaluate different treatment outcomes. The aim of this study was to evaluate supervised machine learning algorithms in the c...

A dataset of laryngeal endoscopic images with comparative study on convolution neural network-based semantic segmentation.

International journal of computer assisted radiology and surgery
PURPOSE: Automated segmentation of anatomical structures in medical image analysis is a prerequisite for autonomous diagnosis as well as various computer- and robot-aided interventions. Recent methods based on deep convolutional neural networks (CNN)...

Spinal cord detection in planning CT for radiotherapy through adaptive template matching, IMSLIC and convolutional neural networks.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: The spinal cord is a very important organ that must be protected in treatments of radiotherapy (RT), considered an organ at risk (OAR). Excess rays associated with the spinal cord can cause irreversible diseases in patients ...

Advancing System Performance with Redundancy: From Biological to Artificial Designs.

Neural computation
Redundancy is a fundamental characteristic of many biological processes such as those in the genetic, visual, muscular, and nervous systems, yet its driven mechanism has not been fully comprehended. Until recently, the only understanding of redundanc...

State-Space Representations of Deep Neural Networks.

Neural computation
This letter deals with neural networks as dynamical systems governed by finite difference equations. It shows that the introduction of -many skip connections into network architectures, such as residual networks and additive dense networks, defines ...

Detecting and interpreting myocardial infarction using fully convolutional neural networks.

Physiological measurement
OBJECTIVE: We aim to provide an algorithm for the detection of myocardial infarction that operates directly on ECG data without any preprocessing and to investigate its decision criteria.

Thyroid Diagnosis from SPECT Images Using Convolutional Neural Network with Optimization.

Computational intelligence and neuroscience
Thyroid disease has now become the second largest disease in the endocrine field; SPECT imaging is particularly important for the clinical diagnosis of thyroid diseases. However, there is little research on the application of SPECT images in the comp...

LCR-Net++: Multi-Person 2D and 3D Pose Detection in Natural Images.

IEEE transactions on pattern analysis and machine intelligence
We propose an end-to-end architecture for joint 2D and 3D human pose estimation in natural images. Key to our approach is the generation and scoring of a number of pose proposals per image, which allows us to predict 2D and 3D poses of multiple peopl...

Binomial Logistic Regression and Artificial Neural Network Methods to Classify Opioid-Dependent Subjects and Control Group Using Quantitative EEG Power Measures.

Clinical EEG and neuroscience
Logistic regression (LR) and artificial neural networks (ANNs) are widely referred approaches in medical data classification studies. LR, a statistical fitting model, is suggested in medical problems because of its well-established methodology and co...