AIMC Topic: Neural Networks, Computer

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A semi-supervised learning-based quality evaluation system for digital chest radiographs.

Medical physics
BACKGROUND: Digital radiography is the most commonly utilized medical imaging technique worldwide, and the quality of radiographs plays a crucial role in accurate disease diagnosis. Therefore, evaluating the quality of radiographs is an essential ste...

Enhanced joint hybrid deep neural network explainable artificial intelligence model for 1-hr ahead solar ultraviolet index prediction.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Exposure to solar ultraviolet (UV) radiation can cause malignant keratinocyte cancer and eye disease. Developing a user-friendly, portable, real-time solar UV alert system especially or wearable electronic mobile devices can...

Clustering-based spatial analysis (CluSA) framework through graph neural network for chronic kidney disease prediction using histopathology images.

Scientific reports
Machine learning applied to digital pathology has been increasingly used to assess kidney function and diagnose the underlying cause of chronic kidney disease (CKD). We developed a novel computational framework, clustering-based spatial analysis (Clu...

Insight into ADHD diagnosis with deep learning on Actimetry: Quantitative interpretation of occlusion maps in age and gender subgroups.

Artificial intelligence in medicine
UNLABELLED: Attention Deficit/Hyperactivity Disorder (ADHD) is a prevalent neurodevelopmental disorder in childhood that often persists into adulthood. Objectively diagnosing ADHD can be challenging due to the reliance on subjective questionnaires in...

Patient specific prior cross attention for kV decomposition in paraspinal motion tracking.

Medical physics
BACKGROUND: X-ray image quality is critical for accurate intrafraction motion tracking in radiation therapy.

Species-specific wiring of cortical circuits for small-world networks in the primary visual cortex.

PLoS computational biology
Long-range horizontal connections (LRCs) are conspicuous anatomical structures in the primary visual cortex (V1) of mammals, yet their detailed functions in relation to visual processing are not fully understood. Here, we show that LRCs are key compo...

Breast Tumor Segmentation in DCE-MRI With Tumor Sensitive Synthesis.

IEEE transactions on neural networks and learning systems
Segmenting breast tumors from dynamic contrast-enhanced magnetic resonance (DCE-MR) images is a critical step for early detection and diagnosis of breast cancer. However, variable shapes and sizes of breast tumors, as well as inhomogeneous background...

A Scalable Open-Set ECG Identification System Based on Compressed CNNs.

IEEE transactions on neural networks and learning systems
Deep learning (DL) is known for its excellence in feature learning and its ability to deliver high-accuracy results. Its application to ECG biometric recognition has received increasing interest but is also accompanied by several deficiencies. In thi...

A Novel Approach to Detecting Muscle Fatigue Based on sEMG by Using Neural Architecture Search Framework.

IEEE transactions on neural networks and learning systems
Muscle fatigue detection is of great significance to human physiological activities, but many complex factors increase the difficulty of this task. In this article, we integrate several effective techniques to distinguish muscle states under fatigue ...

Metaparametric Neural Networks for Survival Analysis.

IEEE transactions on neural networks and learning systems
Survival analysis is a critical tool for the modeling of time-to-event data, such as life expectancy after a cancer diagnosis or optimal maintenance scheduling for complex machinery. However, current neural network models provide an imperfect solutio...