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References from Fractal-dimension–based quantification of CT micronodules for classifying borderline pneumoconiosis. Local targets link to admitted publications; unresolved targets remain external evidence.
Coal mine dust lung disease in the modern era
10.1111/resp.13034 · 2017 · External reference
Pneumoconiosis: current status and future prospects
10.1097/cm9.0000000000001461 · 2021 · External reference
Issue 3—The occupational burden of respiratory diseases, an update
10.1016/j.pulmoe.2024.03.004 · 2025 · External reference
Pneumoconiosis redux: Coal workers' pneumoconiosis and silicosis are still a problem
10.1164/rccm.201511-2154ed · 2016 · External reference
Coal mining and lung disease in the 21st century
10.1097/mcp.0000000000000653 · 2020 · External reference
Advances in the study of silica nanoparticles in lung diseases
10.1016/j.scitotenv.2023.169352 · 2024 · External reference
Unresolved reference
2022 · External reference
Unresolved reference
2005 · External reference
Computed tomography findings of arc-welders’ pneumoconiosis: comparison with silicosis
10.1016/j.ejrad.2018.08.020 · 2018 · External reference
Comparison of the international classification of High-resolution computed tomography for occupational and environmental respiratory diseases with the international labor organization international classification of radiographs of pneumoconiosis
10.2486/indhealth.2018-0068 · 2019 · External reference
Differentiation and prediction of pneumoconiosis stage by computed tomography texture analysis based on U-Net neural network
10.1016/j.cmpb.2022.107098 · 2022 · External reference
Computed tomography-based imaging biomarker identifies coal workers' pneumoconiosis
10.3389/fphys.2023.1288246 · 2023 · External reference
Identification of high-risk population of pneumoconiosis using deep learning segmentation of lung 3D images and radiomics texture analysis
10.1016/j.cmpb.2024.108006 · 2024 · External reference
Machine learning models for the prediction of preclinical coal workers' pneumoconiosis: integrating CT radiomics and occupational health surveillance records
10.1186/s12967-025-06907-3 · 2025 · External reference
The image biomarker standardization initiative: standardized quantitative radiomics for high-throughput image-based phenotyping
10.1148/radiol.2020191145 · 2020 · External reference
Self-affine fractals and fractal dimension
10.1088/0031-8949/32/4/001 · 1985 · External reference
Fractal and multifractal analysis: a review
10.1016/j.media.2009.05.003 · 2009 · External reference
TotalSegmentator: robust segmentation of 104 anatomic structures in CT images
10.1148/ryai.230024 · 2023 · External reference
Learning tubule-sensitive CNNs for pulmonary airway and artery-vein segmentation in CT
10.1109/tmi.2021.3062280 · 2021 · External reference
R: A language for data analysis and graphics
10.1080/10618600.1996.10474713 · 1996 · External reference
Unresolved reference
1998 · External reference
Scikit-learn: machine learning in python
2011 · External reference
Harmonization of large MRI datasets for the analysis of brain imaging patterns throughout the lifespan
10.1016/j.neuroimage.2019.116450 · 2020 · External reference
Calibration: the Achilles heel of predictive analytics
10.1186/s12916-019-1466-7 · 2019 · External reference
