Improved Hidden Markov Algorithm Based on Bayes in Low Dose CT Images

This study reviews methods to ensure the reliability of machine learning models in medical imaging, focusing on bias detection, data drift assessment, and accuracy estimation without ground truth labels to enhance integration into clinical settings.

In this study, the authors used a Bayesian Poisson - Hidden Markov Model to analyze COVID-19 cases by blood type in Europe and Africa, finding differences in hidden states and infection rates based on blood type across these regions.

This study proposes a new method for hair segmentation that improved performance in skin lesion images, as indicated by an increase in the Dice score from 76.97% to 79.08%.

This study found that the proposed Bayesian measurement-error-driven hidden Markov regression model effectively calibrated inflated covariate effect sizes in a community-based survey on androgenetic alopecia regardless of misclassification type.

This study introduces a novel image enhancement method that significantly improves the visual quality of low-light sports images by utilizing improved bilateral filtering and the CLAHE algorithm, achieving a 65.24% improvement in color and edge detail preservation compared to state-of-the-art methods on the LOL dataset.