AIMC Topic: Deep Learning

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Deep learning-based classification of kidney transplant pathology: a retrospective, multicentre, proof-of-concept study.

The Lancet. Digital health
BACKGROUND: Histopathological assessment of transplant biopsies is currently the standard method to diagnose allograft rejection and can help guide patient management, but it is one of the most challenging areas of pathology, requiring considerable e...

A CT-based deep learning model for subsolid pulmonary nodules to distinguish minimally invasive adenocarcinoma and invasive adenocarcinoma.

European journal of radiology
OBJECTIVE: To develop and validate a deep learning nomogram (DLN) model constructed from non-contrast computed tomography (CT) images for discriminating minimally invasive adenocarcinoma (MIA) from invasive adenocarcinoma (IAC) in patients with subso...

Human Gait Recognition: A Single Stream Optimal Deep Learning Features Fusion.

Sensors (Basel, Switzerland)
Human Gait Recognition (HGR) is a biometric technique that has been utilized for security purposes for the last decade. The performance of gait recognition can be influenced by various factors such as wearing clothes, carrying a bag, and the walking ...

Deep learning approaches in remote sensing of soil organic carbon: a review of utility, challenges, and prospects.

Environmental monitoring and assessment
The use of neural network (NN) models for remote sensing (RS) retrieval of landscape biophysical and biochemical properties has become popular in the last decade. Recently, the emergence of "big data" that can be generated from remotely sensed data a...

No more glowing in the dark: how deep learning improves exposure date estimation in thermoluminescence dosimetry.

Journal of radiological protection : official journal of the Society for Radiological Protection
The time- or temperature-resolved detector signal from a thermoluminescence dosimeter can reveal additional information about circumstances of an exposure to ionising irradiation. We present studies using deep neural networks to estimate the date of ...

Utilization of Nursing Defect Management Evaluation and Deep Learning in Nursing Process Reengineering Optimization.

Computational and mathematical methods in medicine
It was to explore the application of nursing defect management evaluation and deep learning in nursing process reengineering optimization. This study first selects the root cause analysis method to analyse the nursing defect management, then realizes...

Detection of Fake News Text Classification on COVID-19 Using Deep Learning Approaches.

Computational and mathematical methods in medicine
A vast amount of data is generated every second for microblogs, content sharing via social media sites, and social networking. Twitter is an essential popular microblog where people voice their opinions about daily issues. Recently, analyzing these o...

An Optimized Hybrid Deep Learning Model to Detect COVID-19 Misleading Information.

Computational intelligence and neuroscience
Fake news is challenging to detect due to mixing accurate and inaccurate information from reliable and unreliable sources. Social media is a data source that is not trustworthy all the time, especially in the COVID-19 outbreak. During the COVID-19 ep...

Fully automated deep learning for knee alignment assessment in lower extremity radiographs: a cross-sectional diagnostic study.

Skeletal radiology
OBJECTIVES: Accurate assessment of knee alignment and leg length discrepancy is currently measured manually from standing long-leg radiographs (LLR), a process that is both time consuming and poorly reproducible. The aim was to assess the performance...

Fast T2-Weighted Imaging With Deep Learning-Based Reconstruction: Evaluation of Image Quality and Diagnostic Performance in Patients Undergoing Radical Prostatectomy.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: Deep learning-based reconstruction (DLR) can potentially improve image quality by reduction of noise, thereby enabling fast acquisition of magnetic resonance imaging (MRI). However, a systematic evaluation of image quality and diagnostic ...