AIMC Topic: Neural Networks, Computer

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Machine learning model outperforms the ACS Risk Calculator in predicting non-home discharge following primary total knee arthroplasty.

Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA
PURPOSE: Despite the increase in outpatient total knee arthroplasty (TKA) procedures, many patients are still discharged to non-home locations following index surgery. The ability to accurately predict non-home discharge (NHD) following TKAs has the ...

Transformer-based approaches for neuroimaging: an in-depth review of their role in classification and regression tasks.

Reviews in the neurosciences
In the ever-evolving landscape of deep learning (DL), the transformer model emerges as a formidable neural network architecture, gaining significant traction in neuroimaging-based classification and regression tasks. This paper presents an extensive ...

Classification of psychosis spectrum disorders using graph convolutional networks with structurally constrained functional connectomes.

Neural networks : the official journal of the International Neural Network Society
This article considers the problem of classifying individuals in a dataset of diverse psychosis spectrum conditions, including persons with subsyndromal psychotic-like experiences (PLEs) and healthy controls. This task is more challenging than the tr...

Exploring the categorical nature of colour perception: Insights from artificial networks.

Neural networks : the official journal of the International Neural Network Society
The electromagnetic spectrum of light from a rainbow is a continuous signal, yet we perceive it vividly in several distinct colour categories. The origins and underlying mechanisms of this phenomenon remain partly unexplained. We investigate categori...

The role of artificial intelligence in macular hole management: A scoping review.

Survey of ophthalmology
We focus on the utility of artificial intelligence (AI) in the management of macular hole (MH). We synthesize 25 studies, comprehensively reporting on each AI model's development strategy, validation, tasks, performance, strengths, and limitations. A...

Reconstructing and analyzing the invariances of low-dose CT image denoising networks.

Medical physics
BACKGROUND: Deep learning-based methods led to significant advancements in many areas of medical imaging, most of which are concerned with the reduction of artifacts caused by motion, scatter, or noise. However, with most neural networks being black ...

Augmented histopathology: Enhancing colon cancer detection through deep learning and ensemble techniques.

Microscopy research and technique
Colon cancer poses a significant threat to human life with a high global mortality rate. Early and accurate detection is crucial for improving treatment quality and the survival rate. This paper presents a comprehensive approach to enhance colon canc...

Neural Memory State Space Models for Medical Image Segmentation.

International journal of neural systems
With the rapid advancement of deep learning, computer-aided diagnosis and treatment have become crucial in medicine. UNet is a widely used architecture for medical image segmentation, and various methods for improving UNet have been extensively explo...

Machine learning algorithms for the evaluation of risk by tick-borne pathogens in Europe.

Annals of medicine
BACKGROUND: Tick-borne pathogens pose a major threat to human health worldwide. Understanding the epidemiology of tick-borne diseases to reduce their impact on human health requires models covering large geographic areas and considering both the abio...

A Lightweight Convolutional Neural Network-Reformer Model for Efficient Epileptic Seizure Detection.

International journal of neural systems
A real-time and reliable automatic detection system for epileptic seizures holds significant value in assisting physicians with rapid diagnosis and treatment of epilepsy. Aiming to address this issue, a novel lightweight model called Convolutional Ne...