AIMC Topic: Neural Networks, Computer

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A hybrid inception-dilated-ResNet architecture for deep learning-based prediction of COVID-19 severity.

Scientific reports
Chest computed tomography (CT) scans are essential for accurately assessing the severity of the novel Coronavirus (COVID-19), facilitating appropriate therapeutic interventions and monitoring disease progression. However, determining COVID-19 severit...

SVEA: an accurate model for structural variation detection using multi-channel image encoding and enhanced AlexNet architecture.

Journal of translational medicine
BACKGROUND: Structural variations (SVs) are a pervasive and impactful class of genetic variation within the genome, significantly influencing gene function, impacting human health, and contributing to disease. Recent advances in deep learning have sh...

Event-driven figure-ground organisation model for the humanoid robot iCub.

Nature communications
Figure-ground organisation is a perceptual grouping mechanism for detecting objects and boundaries, essential for an agent interacting with the environment. Current figure-ground segmentation methods rely on classical computer vision or deep learning...

Optimizing thermal dose prediction in nanoparticle-mediated photothermal therapy using a convolutional neural network-based model.

Journal of thermal biology
Nanoparticle-mediated photothermal therapy (NMPTT) is an up-and-coming targeted cancer treatment. Here, nanoparticles are used to convert near-infrared light into localized heat that can kill tumour cells while sparing surrounding healthy tissue. Nev...

NPI-HGNN: A Heterogeneous Graph Neural Network-Based Approach for Predicting ncRNA-Protein Interactions.

Interdisciplinary sciences, computational life sciences
Accurate identification of ncRNA-protein interactions (NPIs) is critical for understanding various cellular activities and biological functions of ncRNAs and proteins. Many sequence- and/or structure- and graph-based computational approaches have bee...

Self-triggered neural tracking control for discrete-time nonlinear systems via adaptive critic learning.

Neural networks : the official journal of the International Neural Network Society
In this paper, a novel self-triggered optimal tracking control method is developed based on the online action-critic technique for discrete-time nonlinear systems. First, an augmented plant is constructed by integrating the system state with the refe...

Large-scale benchmarking and boosting transfer learning for medical image analysis.

Medical image analysis
Transfer learning, particularly fine-tuning models pretrained on photographic images to medical images, has proven indispensable for medical image analysis. There are numerous models with distinct architectures pretrained on various datasets using di...

Hyperfusion: A hypernetwork approach to multimodal integration of tabular and medical imaging data for predictive modeling.

Medical image analysis
The integration of diverse clinical modalities such as medical imaging and the tabular data extracted from patients' Electronic Health Records (EHRs) is a crucial aspect of modern healthcare. Integrative analysis of multiple sources can provide a com...

Deep learning combined Monte Carlo simulation reveal the fundamental light propagation in apple puree: Monitoring the quality changes from different cultivar, storage period and heating duration.

Food research international (Ottawa, Ont.)
This work explored the light propagation of purees from a large variability of apple cultivar, storage period and heating duration based on their optical absorption (μ) and reduced scattering (μ') properties at 900-1650 nm, in order to better monitor...

Integrating NLP and LLMs to discover biomarkers and mechanisms in Alzheimer's disease.

SLAS technology
Alzheimer's disease (AD) is a progressive neurological condition characterized by cognitive decline, memory loss, and aberrant behaviour. It affects millions of people globally and is one of the main causes of dementia. The neurodegenerative conditio...