AIMC Topic: Deep Learning

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Total synchronous fluorescence spectroscopy coupled with deep learning to rapidly identify the authenticity of sesame oil.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
The quality of sesame oil (SO) has been paid more and more attention. In this study, total synchronous fluorescence (TSyF) spectroscopy and deep neural networks were utilized to identify counterfeit and adulterated sesame oils. Firstly, typical sampl...

Toward automated severe pharyngitis detection with smartphone camera using deep learning networks.

Computers in biology and medicine
PURPOSE: Severe pharyngitis is frequently associated with inflammations caused by streptococcal pharyngitis, which can cause immune-mediated and post-infectious complications. The recent global pandemic of coronavirus disease (COVID-19) encourages th...

Predicting bovine tuberculosis status of dairy cows from mid-infrared spectral data of milk using deep learning.

Journal of dairy science
Bovine tuberculosis (bTB) is a zoonotic disease in cattle that is transmissible to humans, distributed worldwide, and considered endemic throughout much of England and Wales. Mid-infrared (MIR) analysis of milk is used routinely to predict fat and pr...

Efficient and Effective Training of COVID-19 Classification Networks With Self-Supervised Dual-Track Learning to Rank.

IEEE journal of biomedical and health informatics
Coronavirus Disease 2019 (COVID-19) has rapidly spread worldwide since first reported. Timely diagnosis of COVID-19 is crucial both for disease control and patient care. Non-contrast thoracic computed tomography (CT) has been identified as an effecti...

Using autoencoders as a weight initialization method on deep neural networks for disease detection.

BMC medical informatics and decision making
BACKGROUND: As of today, cancer is still one of the most prevalent and high-mortality diseases, summing more than 9 million deaths in 2018. This has motivated researchers to study the application of machine learning-based solutions for cancer detecti...

Automatic lung segmentation in routine imaging is primarily a data diversity problem, not a methodology problem.

European radiology experimental
BACKGROUND: Automated segmentation of anatomical structures is a crucial step in image analysis. For lung segmentation in computed tomography, a variety of approaches exists, involving sophisticated pipelines trained and validated on different datase...

Improved dual-scale residual network for image super-resolution.

Neural networks : the official journal of the International Neural Network Society
In recent years, convolutional neural networks have been successfully applied to single image super-resolution (SISR) tasks, making breakthrough progress both in accuracy and speed. In this work, an improved dual-scale residual network (IDSRN), achie...

Identifying resting-state effective connectivity abnormalities in drug-naïve major depressive disorder diagnosis via graph convolutional networks.

Human brain mapping
Major depressive disorder (MDD) is a leading cause of disability; its symptoms interfere with social, occupational, interpersonal, and academic functioning. However, the diagnosis of MDD is still made by phenomenological approach. The advent of neuro...

Linear versus deep learning methods for noisy speech separation for EEG-informed attention decoding.

Journal of neural engineering
OBJECTIVE: A hearing aid's noise reduction algorithm cannot infer to which speaker the user intends to listen to. Auditory attention decoding (AAD) algorithms allow to infer this information from neural signals, which leads to the concept of neuro-st...

Extracting diffusion tensor fractional anisotropy and mean diffusivity from 3-direction DWI scans using deep learning.

Magnetic resonance in medicine
PURPOSE: To develop and evaluate machine-learning methods that reconstruct fractional anisotropy (FA) values and mean diffusivities (MD) from 3-direction diffusion MRI (dMRI) acquisitions.