AIMC Topic: Neural Networks, Computer

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Unsupervised motion artifact correction of turbo spin-echo MRI using deep image prior.

Magnetic resonance in medicine
PURPOSE: In MRI, motion artifacts can significantly degrade image quality. Motion artifact correction methods using deep neural networks usually required extensive training on large datasets, making them time-consuming and resource-intensive. In this...

Prediction of molecular-specific mutagenic alerts and related mechanisms of chemicals by a convolutional neural network (CNN) model based on SMILES split.

The Science of the total environment
Structural alerts (SAs) are essential to identify chemicals for toxicity evaluation and health risk assessment. We constructed a novel SMILES split-based deep learning model (SSDL) that was trained and verified with 5850 chemicals from the ISSSTY dat...

MFD-GDrug: multimodal feature fusion-based deep learning for GPCR-drug interaction prediction.

Methods (San Diego, Calif.)
The accurate identification of drug-protein interactions (DPIs) is crucial in drug development, especially concerning G protein-coupled receptors (GPCRs), which are vital targets in drug discovery. However, experimental validation of GPCR-drug pairin...

Using Deep Learning and B-Splines to Model Blood Vessel Lumen from 3D Images.

Sensors (Basel, Switzerland)
Accurate geometric modeling of blood vessel lumen from 3D images is crucial for vessel quantification as part of the diagnosis, treatment, and monitoring of vascular diseases. Our method, unlike other approaches which assume a circular or elliptical ...

The Effect of Noise on Deep Learning for Classification of Pathological Voice.

The Laryngoscope
OBJECTIVE: This study aimed to evaluate the significance of background noise in machine learning models assessing the GRBAS scale for voice disorders.

M2AI-CVD: Multi-modal AI approach cardiovascular risk prediction system using fundus images.

Network (Bristol, England)
Cardiovascular diseases (CVD) represent a significant global health challenge, often remaining undetected until severe cardiac events, such as heart attacks or strokes, occur. In regions like Qatar, research focused on non-invasive CVD identification...

Development of Artificial Intelligence Image Classification Models for Determination of Umbilical Cord Vascular Anomalies.

Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine
OBJECTIVE: The goal of this work was to develop robust techniques for the processing and identification of SUA using artificial intelligence (AI) image classification models.

Automatedly identify dryland threatened species at large scale by using deep learning.

The Science of the total environment
Dryland biodiversity is decreasing at an alarming rate. Advanced intelligent tools are urgently needed to rapidly, automatedly, and precisely detect dryland threatened species on a large scale for biological conservation. Here, we explored the perfor...

NADOL: Neuromorphic Architecture for Spike-Driven Online Learning by Dendrites.

IEEE transactions on biomedical circuits and systems
Biologically plausible learning with neuronal dendrites is a promising perspective to improve the spike-driven learning capability by introducing dendritic processing as an additional hyperparameter. Neuromorphic computing is an effective and essenti...

Reservoir Computing With Dynamic Reservoir using Cascaded DNA Memristors.

IEEE transactions on biomedical circuits and systems
This article proposes molecular and DNA memristors where the state is defined by a single output variable. In past molecular and DNA memristors, the state of the memristor was defined based on two output variables. These memristors cannot be cascaded...