AIMC Topic: Deep Learning

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Spatial-Frequency Feature Learning and Classification of Motor Imagery EEG Based on Deep Convolution Neural Network.

Computational and mathematical methods in medicine
EEG pattern recognition is an important part of motor imagery- (MI-) based brain computer interface (BCI) system. Traditional EEG pattern recognition algorithm usually includes two steps, namely, feature extraction and feature classification. In feat...

Cascaded one-shot deformable convolutional neural networks: Developing a deep learning model for respiratory motion estimation in ultrasound sequences.

Medical image analysis
Improving the quality of image-guided radiation therapy requires the tracking of respiratory motion in ultrasound sequences. However, the low signal-to-noise ratio and the artifacts in ultrasound images make it difficult to track targets accurately a...

FecalNet: Automated detection of visible components in human feces using deep learning.

Medical physics
PURPOSE: To automate the detection and identification of visible components in feces for early diagnosis of gastrointestinal diseases, we propose FecalNet, a method using multiple deep neural networks.

Diagnosing Heart Failure from Chest X-Ray Images Using Deep Learning.

International heart journal
The development of deep learning technology has enabled machines to achieve high-level accuracy in interpreting medical images. While many previous studies have examined the detection of pulmonary nodules in chest X-rays using deep learning, the appl...

Can Ensemble Deep Learning Identify People by Their Gait Using Data Collected from Multi-Modal Sensors in Their Insole?

Sensors (Basel, Switzerland)
Gait is a characteristic that has been utilized for identifying individuals. As human gait information is now able to be captured by several types of devices, many studies have proposed biometric identification methods using gait information. As rese...

Implementation of a Deep Learning-Based Computer-Aided Detection System for the Interpretation of Chest Radiographs in Patients Suspected for COVID-19.

Korean journal of radiology
OBJECTIVE: To describe the experience of implementing a deep learning-based computer-aided detection (CAD) system for the interpretation of chest X-ray radiographs (CXR) of suspected coronavirus disease (COVID-19) patients and investigate the diagnos...

Incremental Image Noise Reduction in Coronary CT Angiography Using a Deep Learning-Based Technique with Iterative Reconstruction.

Korean journal of radiology
OBJECTIVE: To assess the feasibility of applying a deep learning-based denoising technique to coronary CT angiography (CCTA) along with iterative reconstruction for additional noise reduction.

Neuro-fuzzy patch-wise R-CNN for multiple sclerosis segmentation.

Medical & biological engineering & computing
The segmentation of the lesion plays a core role in diagnosis and monitoring of multiple sclerosis (MS). Magnetic resonance imaging (MRI) is the most frequent image modality used to evaluate such lesions. Because of the massive amount of data, manual...

Risk management system and intelligent decision-making for prefabricated building project under deep learning modified teaching-learning-based optimization.

PloS one
This study establishes a model of prefabricated building project risk management system based on the Modified Teaching-Learning-Based-Optimization (MTLBO) algorithm and a prediction model of deep learning multilayer feedforward neural network (Backpr...