AIMC Topic: Neural Networks, Computer

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High-level visual processing in the lateral geniculate nucleus revealed using goal-driven deep learning.

Journal of neuroscience methods
BACKGROUND: The Lateral Geniculate Nucleus (LGN) is an essential contributor to high-level visual processing despite being an early subcortical area in the visual system. Current LGN computational models focus on its basic properties, with less empha...

Application effect of rehabilitation robots in rehabilitation of limb movement disorders based on neural network algorithms.

SLAS technology
With the continuous advancement of computer technology and sensor technology, rehabilitation robots have shown great potential in the rehabilitation treatment of limb movement disorders. This paper designs a rehabilitation robot based on a neural net...

Forewarning the seasonal dynamics of corn leafhopper and mollicutes through neural networks.

International journal of biometeorology
The corn leafhopper (CL), Dalbulus maidis (DeLong & Wolcott) (Hemiptera: Cicadellidae), has become the most important corn pest in Brazil and other corn-producing countries. This highly efficient insect vector transmits corn stunting pathogens result...

A graph neural network explainability strategy driven by key subgraph connectivity.

Journal of biomedical informatics
Current explainability strategies for Graph Neural Networks (GNNs) often focus on individual nodes or edges, neglecting the significance of key subgraphs in decision-making processes. This limitation can result in dispersed and less reliable explanat...

SegElegans: Instance segmentation using dual convolutional recurrent neural network decoder in Caenorhabditis elegans microscopic images.

Computers in biology and medicine
Caenorhabditis elegans is a great model for exploring organismal, cellular, and subcellular biology through optical and fluorescence microscopy, with its research applications steadily expanding. However, manual processing of numerous microscopic ima...

Thinking Like Sonographers: Human-Centered CNN Models for Gout Diagnosis From Musculoskeletal Ultrasound.

IEEE transactions on bio-medical engineering
We explore the potential of deep convolutional neural network (CNN) models for differential diagnosis of gout from musculoskeletal ultrasound (MSKUS). Our exhaustive study of state-of-the-art (SOTA) CNN image classification models for this problem re...

Deep Learning-Based Event Counting for Apnea-Hypopnea Index Estimation Using Recursive Spiking Neural Networks.

IEEE transactions on bio-medical engineering
OBJECTIVE: To develop a novel method for improved screening of sleep apnea in home environments, focusing on reliable estimation of the Apnea-Hypopnea Index (AHI) without the need for highly precise event localization.

A Hybrid ODE-NN Framework for Modeling Incomplete Physiological Systems.

IEEE transactions on bio-medical engineering
This paper proposes a method to learn approximations of missing Ordinary Differential Equations (ODEs) and states in physiological models where knowledge of the system's relevant states and dynamics is incomplete. The proposed method augments known O...

PULSE: A DL-Assisted Physics-Based Approach to the Inverse Problem of Electrocardiography.

IEEE transactions on bio-medical engineering
This study introduces an innovative approach combining deep-learning techniques with classical physics-based electrocardiographic imaging (ECGI) methods. Our objective is to enhance the accuracy and robustness of ECGI reconstructions. We reshape the ...

Bulldogs stenosis degree classification using synthetic images created by generative artificial intelligence.

Scientific reports
Nasal stenosis in bulldogs significantly impacts their quality of life, making early diagnosis crucial for effective treatment. This study developed an automated deep learning model to classify the severity of nasal stenosis using 1020 images of bull...