AIMC Topic: Deep Learning

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Analysis of Regional Financial Risk in Guangdong Province Based on the DCN Deep Learning Model.

Computational intelligence and neuroscience
In the free flow of financial factors oriented to capital, returns will be accompanied by the concentration and diffusion of financial resources to form regional financial spatial differences, which is an objective phenomenon of regional financial pr...

Design of Human Resource Management System Based on Deep Learning.

Computational intelligence and neuroscience
With the advent of the Internet era, the frequency and proportion of candidates obtaining recruitment information through the Internet is getting higher and higher, and the amount of human resources information such as talent information and postinfo...

Design of Moving Target Detection System Using Lightweight Deep Learning Model and Its Impact on the Development of Sports Industry.

Computational intelligence and neuroscience
The intelligent tracking and detection of athletes' actions and the improvement of action standardization are of great practical significance to reducing the injury caused by sports in the sports industry. For the problems of nonstandard movement and...

Generalizability of Deep Learning Segmentation Algorithms for Automated Assessment of Cartilage Morphology and MRI Relaxometry.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: Deep learning (DL)-based automatic segmentation models can expedite manual segmentation yet require resource-intensive fine-tuning before deployment on new datasets. The generalizability of DL methods to new datasets without fine-tuning i...

Deep Learning to Predict Neonatal and Infant Brain Age from Myelination on Brain MRI Scans.

Radiology
Background Assessment of appropriate brain myelination on T1- and T2-weighted MRI scans is based on gestationally corrected age (GCA) and requires subjective visual inspection of the brain with knowledge of normal myelination milestones. Purpose To d...

Quantitative evaluation of a deep learning-based framework to generate whole-body attenuation maps using LSO background radiation in long axial FOV PET scanners.

European journal of nuclear medicine and molecular imaging
PURPOSE: Attenuation correction is a critically important step in data correction in positron emission tomography (PET) image formation. The current standard method involves conversion of Hounsfield units from a computed tomography (CT) image to cons...

Simplified Transfer Learning for Chest Radiography Models Using Less Data.

Radiology
Background Developing deep learning models for radiology requires large data sets and substantial computational resources. Data set size limitations can be further exacerbated by distribution shifts, such as rapid changes in patient populations and s...

Academic self-concept mediates the effect of online learning engagement on deep learning in online courses for Chinese nursing students: A cross-sectional study.

Nurse education today
BACKGROUND: Online learning is prevalent among nursing students, but the effect of online learning seems not as good as expected. Deep learning, as a learning approach that could help people solve complex problems and make innovative decisions, is as...

Histologic Screening of Malignant Melanoma, Spitz, Dermal and Junctional Melanocytic Nevi Using a Deep Learning Model.

The American Journal of dermatopathology
OBJECTIVE: The integration of an artificial intelligence tool into pathologists' workflow may lead to a more accurate and timely diagnosis of melanocytic lesions, directly patient care. The objective of this study was to create and evaluate the perfo...

An efficient deep learning-based framework for tuberculosis detection using chest X-ray images.

Tuberculosis (Edinburgh, Scotland)
Early diagnosis of tuberculosis (TB) is an essential and challenging task to prevent disease, decrease mortality risk, and stop transmission to other people. The chest X-ray (CXR) is the top choice for lung disease screening in clinics because it is ...