L. Zhang, et al. A deep learning-based model for screening and staging pneumoconiosis, Scientific reports, vol. 11, no. 1, pp.1-7, 2021.

D. Blackley, C. Halldin, A. Laney. Continued increase in prevalence of coal workers’ pneumoconiosis in the United States, 1970–2017, American journal of public health, vol. 108, no. 9, pp. 1220-1222, 2018.

O. Eiichiro, I. Kawashita, and T. Ishida. Development of CAD based on ANN analysis of power spectra for pneumoconiosis in chest radiographs: effect of three new enhancement methods, Radiological physics and technology, vol. 7, no. 2, pp. 217-227, 2014.

C. Spampinato, et al. Deep learning for automated skeletal bone age assessment in X-ray images, Medical image analysis, vol. 36, pp.41-51, 2017.

Sun, Wenjian, et al. A fully deep learning paradigm for pneumoconiosis staging on chest radiographs. IEEE Journal of Biomedical and Health Informatics, vol. 26, no. 10, pp. 5154-5164, 2022.

A. Krizhevsky, I. Sutskever, and G. Hinton. Imagenet classification with deep convolutional neural networks, Communications of the ACM, vol. 60, no. 6, pp.84-90, 2017.

A. John, et al. Representation learning for mammography mass lesion classification with convolutional neural networks, Computer methods and programs in biomedicine, vol. 127, pp.248-257, 2016.

J. Zhang, X. Zhang. Multi-branch convolutional neural network classification method for pulmonary nodules and its interpretability, Computer Science, vol. 47, no. 9, pp.129-134, 2020.

C. Shorten, and T. Khoshgoftaar. A survey on image data augmentation for deep learning, Journal of big data, vol. 6, no. 1, pp.1-48, 2019.

P. Rajpurkar, et al. Deep learning for chest radiograph diagnosis: A retrospective comparison of the CheXNeXt algorithm to practicing radiologists, PLoS medicine, vol. 15, no. 11, pp.e1002686, 2018.

I. Pan, S. Agarwal, and D. Merck. Generalizable inter-institutional classification of abnormal chest radiographs using efficient convolutional neural networks, Journal of digital imaging, vol. 32, no. 5, pp.888-896, 2019.

K. He, J. Sun, and X. Tang. Single image haze removal using dark channel prior, IEEE transactions on pattern analysis and machine intelligence, vol. 33, no. 12, pp. 2341-2353, 2010.

W. Yang, et al. Predicting CT Image From MRI Data Through Feature Matching with Learned Nonlinear Local Descriptors, IEEE Transactions on Medical Imaging, vol. 37, no. 4, pp. 977-987, April 2018.

M. Geng, et al. Content-Noise Complementary Learning for Medical Image Denoising, IEEE Transactions on Medical Imaging, vol. 41, no. 2, pp. 407-419, Feb. 2022.

Rui, Wang, and Wang Guoyu. Medical X-ray image enhancement method based on dark channel prior. Proceedings of the 5th International Conference on Bioinformatics and Computational Biology. 2017.

S. Binay, et al. Does periodic lung screening of films meets standards? Pakistan Journal of Medical Sciences, vol. 32, no. 6, pp.1506, 2016.

L. Raymond, and S. Wintermeyer. Medical surveillance of workers exposed to crystalline silica, Journal of Occupational and Environmental Medicine, vol. 48, no. 1, pp.95-101, 2006.

R. Fattal. Single image dehazing, ACM transactions on graphics, vol. 27, no. 3, pp.1-9, 2008.

S. Narasimhan, and S. Nayar. Chromatic framework for vision in bad weather, In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition., 2000, pp.598-605.

R. Tan. Visibility in bad weather from a single image, In Proceedings of the IEEE conference on computer vision and pattern recognition, 2008, pp.1-8.

Cai Z C. Comprehension of GBZ 70-2015 Diagnosis of Occupational Pneumoconiosis. Chinese journal of industrial hygiene and occupational diseases, vol. 34, no. 11, pp. 866-867, 2016.

Q. Luo, et al. A Bi-branch Dark Channel Differential Convolutional Neural Network for Occupational Pneumoconiosis Staging, In Proceedings of the International Joint Conference on Neural Networks, 2022, pp.1-7.

V. Vishnevskiy, T. Gass, G. Szekely, C. Tanner and O. Goksel. Isotropic Total Variation Regularization of Displacements in Parametric Image Registration, IEEE Transactions on Medical Imaging, vol. 36, no. 2, pp. 385-395, Feb. 2017

X. Dong, et al. Abandoning the Bayer-Filter To See in the Dark, In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp.17431-17440.

V. Nair, and G. Hinton. Rectified linear units improve restricted boltzmann machines, In Proceedings of the IEEE conference on Machine Learning, 2010.

F. Li, R. Fergus, and P. Perona. Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories, conference on computer vision and pattern recognition workshop, 2004, pp.178-178.

L. Breiman. Random forests, Machine learning, vol. 45, no. 1, pp.5-32, 2001.

A. Krizhevsky, and G. Hinton. Convolutional deep belief networks on cifar-10, Unpublished manuscript, vol. 40, no. 7, pp.1-9, 2010.

Y. Zhang, Computer-aided diagnosis for pneumoconiosis staging based on multi-scale feature mapping. International Journal of Computational Intelligence Systems, vol. 14, no. 1, pp.1-11, 2021.

J. Cooley, P. Lewis, and P. Welch. The fast Fourier transform and its applications, IEEE Transactions on Education, vol. 12, no. 1, pp.27-34, 1969.

D. Blackley, et al. Progressive massive fibrosis in coal miners from 3 clinics in Virginia, Jama, vol. 319, no. 5, pp.500-501, 2018.

J. Melendez, et al. Accuracy of an automated system for tuberculosis detection on chest radiographs in high-risk screening, The International Journal of Tuberculosis and Lung Disease, vol. 22, no. 5, pp.567-571, 2018.

Aminzadeh., et al. Imaging Breast Microcalcifications Using Dark-Field Signal in Propagation-Based Phase-Contrast Tomography," IEEE Transactions on Medical Imaging, 2022.

Conover, William Jay. Practical nonparametric statistics. vol. 350. john wiley & sons, 1999.

Hollander, Myles, Douglas A. Wolfe, and Eric Chicken. Nonparametric statistical methods. John Wiley & Sons, 2013.

F. Wilcoxon. Individual comparisons by ranking methods, Breakthroughs in statistics, 1992, pp.196-202.