Neural networks : the official journal of the International Neural Network Society
May 6, 2025
This study investigates the robust synchronization of coupled reaction-diffusion memristive neural networks with parameter uncertainties, internal time delays, and general coupling configurations. The proposed synchronization approach relaxes restric...
European journal of pharmaceutical sciences : official journal of the European Federation for Pharmaceutical Sciences
May 3, 2025
The concept of Design Space (DS), delineated as a region of investigated variables aimed at maintaining product quality, was introduced in the International Conference on Harmonisation (ICH) Q8 as a framework to direct pharmaceutical development. How...
IEEE transactions on neural networks and learning systems
May 2, 2025
Low-dose computed tomography (LDCT) image reconstruction techniques can reduce patient radiation exposure while maintaining acceptable imaging quality. Deep learning (DL) is widely used in this problem, but the performance of testing data (also known...
International journal of medical informatics
Apr 28, 2025
BACKGROUND: Machine learning (ML) models are widely used for predicting patient disposition at emergency department (ED) triage. However, these models generate predictions regardless of the level of uncertainty, potentially leading to overconfident o...
Precise segmentation and uncertainty estimation are crucial for error identification and correction in medical diagnostic assistance. Existing methods mainly rely on pixel-wise uncertainty estimations. They (1) neglect the global context, leading to ...
Computer methods and programs in biomedicine
Apr 27, 2025
BACKGROUND AND OBJECTIVE: Semi-supervised medical image segmentation is a class of machine learning paradigms for segmentation model training and inference using both labeled and unlabeled medical images, which can effectively reduce the data labelin...
Neural networks : the official journal of the International Neural Network Society
Apr 24, 2025
Long-term power load forecasting is critical for power system planning but is constrained by intricate temporal patterns. Transformer-based models emphasize modeling long- and short-term dependencies yet encounter limitations from complexity and para...
Aortic stenosis (AS) is a prevalent heart valve disease that requires accurate and timely diagnosis for effective treatment. Current methods for automated AS severity classification rely on black-box deep learning techniques, which suffer from a low ...
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...
OBJECTIVES: To propose and evaluate a novel deep learning model for directly estimating pharmacokinetic (PK) parameter maps and uncertainty estimation from DCE-MRI.
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