AIMC Topic: Neural Networks, Computer

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Magnetic Resonance Imaging Image Feature Analysis Algorithm under Convolutional Neural Network in the Diagnosis and Risk Stratification of Prostate Cancer.

Journal of healthcare engineering
This work aimed to explore the accuracy of magnetic resonance imaging (MRI) images based on the convolutional neural network (CNN) algorithm in the diagnosis of prostate cancer patients and tumor risk grading. A total of 89 patients with prostate can...

DeepSENSE: Learning coil sensitivity functions for SENSE reconstruction using deep learning.

Magnetic resonance in medicine
PURPOSE: To improve the estimation of coil sensitivity functions from limited auto-calibration signals (ACS) in SENSE-based reconstruction for brain imaging.

ResGANet: Residual group attention network for medical image classification and segmentation.

Medical image analysis
In recent years, deep learning technology has shown superior performance in different fields of medical image analysis. Some deep learning architectures have been proposed and used for computational pathology classification, segmentation, and detecti...

Deep neural network enabled corrective source term approach to hybrid analysis and modeling.

Neural networks : the official journal of the International Neural Network Society
In this work, we introduce, justify and demonstrate the Corrective Source Term Approach (CoSTA)-a novel approach to Hybrid Analysis and Modeling (HAM). The objective of HAM is to combine physics-based modeling (PBM) and data-driven modeling (DDM) to ...

Learning Spatial-Spectral-Temporal EEG Representations with Deep Attentive-Recurrent-Convolutional Neural Networks for Pain Intensity Assessment.

Neuroscience
Electroencephalogram (EEG)-based quantitative pain measurement is valuable in the field of clinical pain treatment, providing objective pain intensity assessment especially for nonverbal patients who are unable to self-report. At present, a key chall...

Towards human-level performance on automatic pose estimation of infant spontaneous movements.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Assessment of spontaneous movements can predict the long-term developmental disorders in high-risk infants. In order to develop algorithms for automated prediction of later disorders, highly precise localization of segments and joints by infant pose ...

Automatic deep learning-based myocardial infarction segmentation from delayed enhancement MRI.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Delayed Enhancement cardiac MRI (DE-MRI) has become indispensable for the diagnosis of myocardial diseases. However, to quantify the disease severity, doctors need time to manually annotate the scar and myocardium. To address this issue, in this pape...

A Soft Sensor for Measuring the Wear of an Induction Motor Bearing by the Park's Vector Components of Current and Voltage.

Sensors (Basel, Switzerland)
This paper presents a methodology for creating a soft sensor for predicting the bearing wear of electrical machines. The technique is based on a combination of Park vector methods and a classifier based on an artificial neural network (ANN-classifier...

Plugin Framework-Based Neuro-Symbolic Grounded Task Planning for Multi-Agent System.

Sensors (Basel, Switzerland)
As the roles of robots continue to expand in general, there is an increasing demand for research on automated task planning for a multi-agent system that can independently execute tasks in a wide and dynamic environment. This study introduces a plugi...

DCNet: Densely Connected Deep Convolutional Encoder-Decoder Network for Nasopharyngeal Carcinoma Segmentation.

Sensors (Basel, Switzerland)
Nasopharyngeal Carcinoma segmentation in magnetic resonance imagery (MRI) is vital to radiotherapy. Exact dose delivery hinges on an accurate delineation of the gross tumor volume (GTV). However, the large-scale variation in tumor volume is intractab...