AIMC Topic: Algorithms

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Causal recurrent intervention for cross-modal cardiac image segmentation.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Cross-modal cardiac image segmentation is essential for cardiac disease analysis. In diagnosis, it enables clinicians to obtain more precise information about cardiac structure or function for potential signs by leveraging specific imaging modalities...

Using the Geriatric Emergency Perioperative Risk Index Derived From Artificial Intelligence Algorithms to Predict Outcomes of Geriatric Emergency General Surgery.

The Journal of surgical research
INTRODUCTION: The objective of this study was to employ artificial intelligence (AI) technology for the development of a model that can accurately forecast the outcome of emergency general surgery (EGS) in elderly patients. Additionally, an innovativ...

A machine learning model for predicting severe mycoplasma pneumoniae pneumonia in school-aged children.

BMC infectious diseases
OBJECTIVE: To develop an interpretable machine learning (ML) model for predicting severe Mycoplasma pneumoniae pneumonia (SMPP) in order to provide reliable factors for predicting the clinical type of the disease.

Leveraging vision transformers and entropy-based attention for accurate micro-expression recognition.

Scientific reports
Micro-expressions are difficult to fake and inherently truthful, making micro-expression recognition technology widely applicable across various domains. With the development of artificial intelligence, the accuracy and efficiency of micro-expression...

Deep learning-based recognition model of football player's technical action behavior using PCA-LBP algorithm.

Scientific reports
Football is a sport that requires sportsmen to have both physical strength and physical features. It must consider the distinctions between individuals and then provide targeted training. Football players can perform better on the field with targeted...

Artificial intelligence-powered four-fold upscaling of human brain synthetic metabolite maps.

The Journal of international medical research
ObjectiveCompared with anatomical magnetic resonance imaging modalities, metabolite images from magnetic resonance spectroscopic imaging often suffer from low quality and detail due to their larger voxel sizes. Conventional interpolation techniques a...

Empowering entity synonym set generation using flexible perceptual field and multi-layer contextual information.

PloS one
Automatic generation of entity synonyms plays a pivotal role in various natural language processing applications, such as search engines, question-answering systems, and taxonomy construction. Previous research on generating entity synonym sets has t...

Enhanced convolutional neural network accelerators with memory optimization for routing applications.

PloS one
Currently, Convolutional Neural Networks (CNN) accelerators find application in various digital domains, each highlighting memory utilization as a significant concern leading to system degradation. In response, our present work focuses on optimizing ...

Uncertainty mapping and probabilistic tractography using Simulation-based Inference in diffusion MRI: A comparison with classical Bayes.

Medical image analysis
Simulation-Based Inference (SBI) has recently emerged as a powerful framework for Bayesian inference: Neural networks are trained on simulations from a forward model, and learn to rapidly estimate posterior distributions. We here present an SBI frame...

Sharper insights: Adaptive ellipse-template for robust fovea localization in challenging retinal landscapes.

Computers in biology and medicine
Automated identification of retinal landmarks, particularly the fovea is crucial for diagnosing diabetic retinopathy and other ocular diseases. But accurate identification is challenging due to varying contrast, color irregularities, anatomical struc...