AIMC Topic: Deep Learning

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Jointly estimating bias field and reconstructing uniform MRI image by deep learning.

Journal of magnetic resonance (San Diego, Calif. : 1997)
Bias field is one of the main artifacts that degrade the quality of magnetic resonance images. It introduces intensity inhomogeneity and affects image analysis such as segmentation. In this work, we proposed a deep learning approach to jointly estima...

GRIM: A General, Real-Time Deep Learning Inference Framework for Mobile Devices Based on Fine-Grained Structured Weight Sparsity.

IEEE transactions on pattern analysis and machine intelligence
It is appealing but challenging to achieve real-time deep neural network (DNN) inference on mobile devices, because even the powerful modern mobile devices are considered as "resource-constrained" when executing large-scale DNNs. It necessitates the ...

Unmixing Convolutional Features for Crisp Edge Detection.

IEEE transactions on pattern analysis and machine intelligence
This article presents a context-aware tracing strategy (CATS) for crisp edge detection with deep edge detectors, based on an observation that the localization ambiguity of deep edge detectors is mainly caused by the mixing phenomenon of convolutional...

Automatic Detection of Left Ventricular Dilatation and Hypertrophy from Electrocardiograms Using Deep Learning.

International heart journal
Left ventricular dilatation (LVD) and left ventricular hypertrophy (LVH) are risk factors for heart failure, and their detection improves heart failure screening. This study aimed to investigate the ability of deep learning to detect LVD and LVH from...

Using Deep Learning to Predict Minimum Foot-Ground Clearance Event from Toe-Off Kinematics.

Sensors (Basel, Switzerland)
Efficient, adaptive, locomotor function is critically important for maintaining our health and independence, but falls-related injuries when walking are a significant risk factor, particularly for more vulnerable populations such as older people and ...

Deep neural networks allow expert-level brain meningioma segmentation and present potential for improvement of clinical practice.

Scientific reports
Accurate brain meningioma segmentation and volumetric assessment are critical for serial patient follow-up, surgical planning and monitoring response to treatment. Current gold standard of manual labeling is a time-consuming process, subject to inter...

Minimized Computations of Deep Learning Technique for Early Diagnosis of Diabetic Retinopathy Using IoT-Based Medical Devices.

Computational intelligence and neuroscience
Diabetes mellitus is the main cause of diabetic retinopathy, the most common cause of blindness worldwide. In order to slow down or prevent vision loss and degeneration, early detection and treatment are essential. For the purpose of detecting and cl...

A Natural Language Processing (NLP) Evaluation on COVID-19 Rumour Dataset Using Deep Learning Techniques.

Computational intelligence and neuroscience
: Since December 2019, the coronavirus (COVID-19) epidemic has sparked considerable alarm among the general community and significantly affected societal attitudes and perceptions. Apart from the disease itself, many people suffer from anxiety and de...

Deformation Analysis and Research of Building Envelope by Deep Learning Technology under the Reinforcement of the Diaphragm Wall.

Computational intelligence and neuroscience
The safety analysis of underground buildings is the most crucial problem in the construction industry. This work aims to optimize the safety analysis results of the underground building envelope and comprehensively improve the safety of the undergrou...

Prescreening and Triage of COVID-19 Patients Through Chest X-Ray Images Using Deep Learning Model.

Big data
Deep learning models deliver a fast diagnosis during triage prescreening for COVID-19 patients, reducing waiting time for hospital admission during health emergency scenarios. The Ministry of health and family welfare government of India provides gui...