AIMC Topic: Neural Networks, Computer

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Deep learning kidney segmentation with very limited training data using a cascaded convolution neural network.

PloS one
BACKGROUND: Deep learning segmentation requires large datasets with ground truth. Image annotation is time consuming and leads to shortages of ground truth data for clinical imaging. This study is to investigate the feasibility of kidney segmentation...

MRCON-Net: Multiscale reweighted convolutional coding neural network for low-dose CT imaging.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Low-dose computed tomography (LDCT) has become increasingly important for alleviating X-ray radiation damage. However, reducing the administered radiation dose may lead to degraded CT images with amplified mottle noise and n...

Efficient Path Planning for Mobile Robot Based on Deep Deterministic Policy Gradient.

Sensors (Basel, Switzerland)
When a traditional Deep Deterministic Policy Gradient (DDPG) algorithm is used in mobile robot path planning, due to the limited observable environment of mobile robots, the training efficiency of the path planning model is low, and the convergence s...

Automated segmentation of whole-body CT images for body composition analysis in pediatric patients using a deep neural network.

European radiology
OBJECTIVES: To develop an automatic segmentation algorithm using a deep neural network with transfer learning applicable to whole-body PET-CT images in children.

Compare the performance of multiple binary classification models in microbial high-throughput sequencing datasets.

The Science of the total environment
The development of machine learning and deep learning provided solutions for predicting microbiota response on environmental change based on microbial high-throughput sequencing. However, there were few studies specifically clarifying the performance...

Multivariate time series forecasting method based on nonlinear spiking neural P systems and non-subsampled shearlet transform.

Neural networks : the official journal of the International Neural Network Society
Multivariate time series forecasting remains a challenging task because of its nonlinear, non-stationary, high-dimensional, and spatial-temporal characteristics, along with the dependence between variables. To address this limitation, we propose a no...

RETRACTED: Chemistry-Informed Neural Networks modelling of lignocellulosic biomass pyrolysis.

Bioresource technology
This article has been retracted: please see Elsevier Policy on Article Withdrawal (http://www.elsevier.com/locate/withdrawalpolicy). This article has been retracted at the request of the authors and the Editor-in-Chief. The article has reused text fr...

An improved CNN-based architecture for automatic lung nodule classification.

Medical & biological engineering & computing
Lung cancer is one of the most critical diseases due to its significant death rate compared to all other types of cancer. The early diagnosis of lung cancer that improves the patient's chance of surviving is mostly done in two phases: screening throu...

Automatic microscopic diagnosis of diseases using an improved UNet++ architecture.

Tissue & cell
Anthrax is a severe infectious disease caused by the Bacillus anthracis bacterium. This paper aims to design and implement a fast and reliable system based on microscopic image processing of patient tissue samples for the automatic diagnosis of anthr...

Formation Control of Automated Guided Vehicles in the Presence of Packet Loss.

Sensors (Basel, Switzerland)
This paper presents the formation tracking problem for non-holonomic automated guided vehicles. Specifically, we focus on a decentralized leader-follower approach using linear quadratic regulator control. We study the impact of communication packet l...