AIMC Topic: Neural Networks, Computer

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Accelerating 3D radial MPnRAGE using a self-supervised deep factor model.

Magnetic resonance in medicine
PURPOSE: To develop a self-supervised and memory-efficient deep learning image reconstruction method for 4D non-Cartesian MRI with high resolution and a large parametric dimension.

MobileTurkerNeXt: investigating the detection of Bankart and SLAP lesions using magnetic resonance images.

Radiological physics and technology
The landscape of computer vision is predominantly shaped by two groundbreaking methodologies: transformers and convolutional neural networks (CNNs). In this study, we aim to introduce an innovative mobile CNN architecture designed for orthopedic imag...

NPI-HetGNN: A Prediction Model of ncRNA-Protein Interactions Based on Heterogeneous Graph Neural Networks.

Interdisciplinary sciences, computational life sciences
Non-coding RNAs (ncRNAs) are one of the components of epigenetic mechanisms that regulates gene expression. Studying ncRNA-protein interactions (NPI) can help to explore a wide range of biological features and related diseases. Traditional NPI resear...

Multi-view based heterogeneous graph contrastive learning for drug-target interaction prediction.

Journal of biomedical informatics
Drug-Target Interaction (DTI) prediction plays a pivotal role in accelerating drug discovery and development by identifying novel interactions between drugs and targets. Most previous studies on Drug-Protein Pair (DPP) networks have primarily focused...

Evaluating the impact of human expertise in human-centered AI: A case study on finger-tapping video analysis for dementia detection.

Computers in biology and medicine
PURPOSE: Human-centered artificial intelligence (AI) plays a crucial role in medical research. This paper evaluates the impact of human expertise in AI systems, using dementia prediction as a case study. Specifically, plasma phospho-tau181 (ptau181) ...

Artificial intelligence for early gastric cancer boundary recognition in NBI and nF-NBI endoscopic images.

Scandinavian journal of gastroenterology
OBJECTIVES: Precise delineation of early gastric cancer (EGC) margins is essential for complete resection during endoscopic submucosal dissection. This study aimed to develop deep learning-based models for EGC boundary detection in narrow-band imagin...

Uncertainty Quantification and Temperature Scaling Calibration for Protein-RNA Binding Site Prediction.

Journal of chemical information and modeling
The black-box nature of deep learning has increasingly drawn attention to the reliability and uncertainty of predictive models. Currently, several uncertainty quantification (UQ) methods have been proposed and successfully applied in the fields of mo...

Data Scaling and Generalization Insights for Medicinal Chemistry Deep Learning Models.

Journal of chemical information and modeling
Predictive models hold considerable promise in enabling the faster discovery of safer, more efficacious therapeutics. To better understand and improve the performance of small-molecule predictive models for drug discovery, we conduct multiple experim...

Artificial Intelligence in Patch Testing: Comprehensive Review of Current Applications and Future Prospects in Dermatology.

JMIR dermatology
BACKGROUND: The integration of artificial intelligence (AI) into patch testing for allergic contact dermatitis (ACD) holds the potential to standardize diagnoses, reduce interobserver variability, and improve overall diagnostic accuracy. However, the...

Development of a neural network-based risk prediction model for mild cognitive impairment in older adults with functional disability.

BMC public health
BACKGROUND: Mild Cognitive Impairment (MCI) is a critical transitional stage between normal aging and Alzheimer's disease, and its early identification is essential for delaying disease progression.