AIMC Topic: Deep Learning

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A safe semi-supervised graph convolution network.

Mathematical biosciences and engineering : MBE
In the semi-supervised learning field, Graph Convolution Network (GCN), as a variant model of GNN, has achieved promising results for non-Euclidean data by introducing convolution into GNN. However, GCN and its variant models fail to safely use the i...

More slices, less truth: effects of different test-set design strategies for magnetic resonance image classification.

Croatian medical journal
AIM: To assess the effects of different test-set design strategies for magnetic resonance (MR) image classification using deep learning.

[A deep learning segmentation model for detecting caries in molar teeth].

Zhonghua yi xue za zhi
This study aimed to build a home use deep learning segmentation model to identify the scope of caries lesions. A total of 494 caries photographs of molars and premolars collected via endoscopy were selected. Subsequently, these photographs were label...

Single-frame 3D lensless microscopic imaging via deep learning.

Optics express
Since the pollen of different species varies in shape and size, visualizing the 3-dimensional structure of a pollen grain can aid in its characterization. Lensless sensing is useful for reducing both optics footprint and cost, while the capability to...

Deep learning-based quasi-continuum theory for structure of confined fluids.

The Journal of chemical physics
Predicting the structural properties of water and simple fluids confined in nanometer scale pores and channels is essential in, for example, energy storage and biomolecular systems. Classical continuum theories fail to accurately capture the interfac...

CNGOD-An improved convolution neural network with grasshopper optimization for detection of COVID-19.

Mathematical biosciences and engineering : MBE
The world is facing the pandemic situation due to a beta corona virus named Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). The disease caused by this virus known as Corona Virus Disease 2019 (COVID-19) has affected the entire world. Th...

Automatic quantification and grading of hip bone marrow oedema in ankylosing spondylitis based on deep learning.

Modern rheumatology
OBJECTIVE: This study has developed a new automatic algorithm for the quantificationy and grading of ankylosing spondylitis (AS)-hip arthritis with magnetic resonance imaging (MRI).

Research on chest radiography recognition model based on deep learning.

Mathematical biosciences and engineering : MBE
With the development of medical informatization and against the background of the spread of global epidemic, the demand for automated chest X-ray detection by medical personnel and patients continues to increase. Although the rapid development of dee...

Development and validation of a deep learning model to predict the survival of patients in ICU.

Journal of the American Medical Informatics Association : JAMIA
BACKGROUND: Patients in the intensive care unit (ICU) are often in critical condition and have a high mortality rate. Accurately predicting the survival probability of ICU patients is beneficial to timely care and prioritizing medical resources to im...

Edge detection in single multimode fiber imaging based on deep learning.

Optics express
We propose a new edge detection scheme based on deep learning in single multimode fiber imaging. In this scheme, we creatively design a novel neural network, whose input is a one-dimensional light intensity sequence, and the output is the edge detect...