AIMC Topic: Uncertainty

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Aggregating soft labels from crowd annotations improves uncertainty estimation under distribution shift.

PloS one Jun 9, 2025
Selecting an effective training signal for machine learning tasks is difficult: expert annotations are expensive, and crowd-sourced annotations may not be reliable. Recent work has demonstrated that learning from a distribution over labels acquired f...
Algorithms Crowdsourcing Uncertainty Humans Machine Learning
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Uncertainty Quantification and Temperature Scaling Calibration for Protein-RNA Binding Site Prediction.

Journal of chemical information and modeling Jun 2, 2025
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...
Neural Networks, Computer Binding Sites RNA-Binding Proteins Temperature Uncertainty RNA Protein Binding Calibration
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VKAD: A novel fault detection and isolation model for uncertainty-aware industrial processes.

Neural networks : the official journal of the International Neural Network Society May 22, 2025
Fault detection and isolation (FDI) are essential for effective monitoring of industrial processes. Modern industrial processes involve dynamic systems characterized by complex, high-dimensional nonlinearities, posing significant challenges for accur...
Neural Networks, Computer Nonlinear Dynamics Uncertainty Industry
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A controlled trial examining large Language model conformity in psychiatric assessment using the Asch paradigm.

BMC psychiatry May 12, 2025
BACKGROUND: Despite significant advances in AI-driven medical diagnostics, the integration of large language models (LLMs) into psychiatric practice presents unique challenges. While LLMs demonstrate high accuracy in controlled settings, their perfor...
Uncertainty Brain Neoplasms Male Child Large Language Models Adolescent Language Humans Female Adult Mental Disorders
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Probabilistic design space exploration and optimization via bayesian approach for a fluid bed drying process.

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...
Uncertainty Technology, Pharmaceutical Desiccation Models, Statistical Bayes Theorem Reproducibility of Results
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Unsupervised Domain Adaptation for Low-Dose CT Reconstruction via Bayesian Uncertainty Alignment.

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...
Algorithms Bayes Theorem Humans Tomography, X-Ray Computed Image Processing, Computer-Assisted Radiation Dosage Deep Learning Unsupervised Machine Learning Uncertainty
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Structural uncertainty estimation for medical image segmentation.

Medical image analysis Apr 28, 2025
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 ...
Uncertainty Humans Neural Networks, Computer Image Processing, Computer-Assisted Image Interpretation, Computer-Assisted Reproducibility of Results Ultrasonography Algorithms
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CUAMT: A MRI semi-supervised medical image segmentation framework based on contextual information and mixed uncertainty.

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...
Humans Image Processing, Computer-Assisted Machine Learning Magnetic Resonance Imaging Uncertainty Algorithms Brain Neoplasms
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ProtoASNet: Comprehensive evaluation and enhanced performance with uncertainty estimation for aortic stenosis classification in echocardiography.

Medical image analysis Apr 24, 2025
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 ...
Uncertainty Echocardiography Severity of Illness Index Aortic Valve Stenosis Image Interpretation, Computer-Assisted Deep Learning Humans Neural Networks, Computer
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Uncertainty mapping and probabilistic tractography using Simulation-based Inference in diffusion MRI: A comparison with classical Bayes.

Medical image analysis Apr 20, 2025
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...
Neural Networks, Computer Algorithms Brain Humans Bayes Theorem Markov Chains Image Interpretation, Computer-Assisted Computer Simulation Reproducibility of Results Diffusion Tensor Imaging Uncertainty Diffusion Magnetic Resonance Imaging Monte Carlo Method White Matter
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