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- A review of uncertainty quantification in medical image analysis: Probabilistic and non-probabilistic methods118
This review offers a comprehensive overview of the prevailing methods proposed to quantify the uncertainty inherent in machine learning models developed for various medical image tasks and explores non-probabilistic approaches, thereby furnishing a more holistic survey of research pertaining to uncertainty quantification for machine learning models.
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- EsurvFusion: An evidential multimodal survival fusion model based on Gaussian random fuzzy numbers6
This work proposes a novel evidential multimodal survival fusion model, EsurvFusion, designed to combine multimodal data at the decision level through an evidence-based decision fusion layer that jointly addresses both data and model uncertainty while incorporating modality-level reliability.
- Spectral-Structured Diffusion for Single-Image Rain Removal–
SpectralDiff is introduced, a spectral-structured diffusion-based framework tailored for single-image rain removal that achieves competitive rain removal performance with improved model compactness and favorable inference efficiency compared to existing diffusion-based approaches.
- A longitudinal cross-sectional study of changes in working attitude among primary healthcare workers across the major COVID-19 outbreaks in China–
These findings show that the effects of pandemic-related stress continue beyond the acute crisis, and sustained mental health support, stronger organisational resources, and long-term workforce policies are needed to protect primary health care workers and support resilient health systems.
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Publication data from OpenAlex; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-11. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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