AIMC Topic: Algorithms

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Enhancing automatic multilabel diagnosis of electrocardiogram signals: A masked transformer approach.

Computers in biology and medicine
BACKGROUND AND OBJECTIVE: Electrocardiogram (ECG) is one of the most important diagnostic tools in clinical applications. Although deep learning models have been widely applied to ECG classification tasks, their accuracy remains limited, especially i...

Mechanobiology-guided machine learning models for predicting long bone fracture healing across diverse scenarios.

Computers in biology and medicine
BACKGROUND: Fracture healing is a complex, time-dependent process governed by biological and mechanical factors, including implant properties. While finite element (FE) modeling provides detailed mechanobiological insights into this process, its comp...

Optimal multimodal feature combination and classifier selection for music-based EEG signal analysis.

Computers in biology and medicine
PURPOSE: Music perception is a fundamental human experience, integral to cognitive and emotional processing, making it a crucial area for neuroscientific investigation. This study examined the neural dynamics underlying music perception and identifie...

Artificial intelligence in cardiac sarcoidosis: ECG, Echo, CPET and MRI.

Current opinion in pulmonary medicine
PURPOSE OF REVIEW: Cardiac sarcoidosis is a form of inflammatory cardiomyopathy that varies in its clinical presentation. It is associated with significant clinical complications such as high-degree atrioventricular block, ventricular tachycardia, he...

A novel recursive transformer-based U-Net architecture for enhanced multi-scale medical image segmentation.

Computers in biology and medicine
BACKGROUND: Automatic medical image segmentation techniques are vital for assisting clinicians in making accurate diagnoses and treatment plans. Although the U-shaped network (U-Net) has been widely adopted in medical image analysis, it still faces c...

Improving prediction of fragility fractures in postmenopausal women using random forest.

Computers in biology and medicine
Osteoporosis is a chronic disease characterized by a progressive decline in bone density and quality, leading to increased bone fragility and a higher susceptibility to fractures, even in response to minimal trauma. Osteoporotic fractures represent a...

An integrated approach for key gene selection and cancer phenotype classification: Improving diagnosis and prediction.

Computers in biology and medicine
The identification of key features and reliable phenotype classification remains pivotal in cancer research, with direct implications for early diagnosis, prognosis, treatment optimization, and cost reduction in healthcare. This study introduces a hy...

AMeta-FD: Adversarial Meta-learning for Few-shot retinal OCT image Despeckling.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Speckle noise in Optical coherence tomography (OCT) images compromises the performance of image analysis tasks such as retinal layer boundary detection. Deep learning algorithms have demonstrated the advantage of being more cost-effective and robust ...

An improved YOLOv7-Tiny method for liquid level detection in medical infusion monitoring.

Computers in biology and medicine
BACKGROUND: Intravenous infusion is a common medical intervention, but the need for constant monitoring of fluid levels increases the psychological burden on patients and the workload on healthcare providers. Intelligent infusion monitoring systems c...

CT-Mamba: A hybrid convolutional State Space Model for low-dose CT denoising.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Low-dose CT (LDCT) significantly reduces the radiation dose received by patients, however, dose reduction introduces additional noise and artifacts. Currently, denoising methods based on convolutional neural networks (CNNs) face limitations in long-r...