AIMC Topic: Humans

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Neural-network-based accelerated safe Q-learning for optimal control of discrete-time nonlinear systems with state constraints.

Neural networks : the official journal of the International Neural Network Society
For unknown nonlinear systems with state constraints, it is difficult to achieve the safe optimal control by using Q-learning methods based on traditional quadratic utility functions. To solve this problem, this article proposes an accelerated safe Q...

Machine Learning Methods Based on Chest CT for Predicting the Risk of COVID-19-Associated Pulmonary Aspergillosis.

Academic radiology
RATIONALE AND OBJECTIVES: To develop and validate a machine learning model based on chest CT and clinical risk factors to predict secondary aspergillus infection in hospitalized COVID-19 patients.

Prediction of Multimorbidity Network Evolution in Middle-Aged and Elderly Population Based on CE-GCN.

Interdisciplinary sciences, computational life sciences
PURPOSE: With the evolving disease spectrum, chronic diseases have emerged as a primary burden and a leading cause of mortality. Due to the aging population and the nature of chronic illnesses, patients often suffer from multimorbidity. Predicting th...

Exploring hyperelastic material model discovery for human brain cortex: Multivariate analysis vs. artificial neural network approaches.

Journal of the mechanical behavior of biomedical materials
The human brain, characterized by its intricate architecture, exhibits complex mechanical properties that underpin its critical functional capabilities. Traditional computational methods, such as finite element analysis, have been instrumental in unc...

An accurate and trustworthy deep learning approach for bladder tumor segmentation with uncertainty estimation.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Although deep learning-based intelligent diagnosis of bladder cancer has achieved excellent performance, the reliability of neural network predicted results may not be evaluated. This study aims to explore a trustworthy AI-b...

Intervening on few-shot object detection based on the front-door criterion.

Neural networks : the official journal of the International Neural Network Society
Most few-shot object detection methods aim to utilize the learned generalizable knowledge from base categories to identify instances of novel categories. The fundamental assumption of these approaches is that the model can acquire sufficient transfer...

Enhancing spatial perception and contextual understanding for 3D dense captioning.

Neural networks : the official journal of the International Neural Network Society
3D dense captioning (3D-DC) transcends traditional 2D image captioning by requiring detailed spatial understanding and object localization, aiming to generate high-quality descriptions for objects within 3D environments. Current approaches struggle w...

Use of deep learning-accelerated T2 TSE for prostate MRI: Comparison with and without hyoscine butylbromide admission.

Magnetic resonance imaging
OBJECTIVE: To investigate the use of deep learning (DL) T2-weighted turbo spin echo (TSE) imaging sequence with deep learning acceleration (T2DL) in prostate MRI regarding the necessity of hyoscine butylbromide (HBB) administration for high image qua...

Association Between Aortic Imaging Features and Impaired Glucose Metabolism: A Deep Learning Population Phenotyping Approach.

Academic radiology
RATIONALE AND OBJECTIVES: Type 2 diabetes is a known risk factor for vascular disease with an impact on the aorta. The aim of this study was to develop a deep learning framework for quantification of aortic phenotypes from magnetic resonance imaging ...

The Evolution of Artificial Intelligence in Nuclear Medicine.

Seminars in nuclear medicine
Nuclear medicine has continuously evolved since its beginnings, constantly improving the diagnosis and treatment of various diseases. The integration of artificial intelligence (AI) is one of the latest revolutionizing chapters, promising significant...