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% BibTeX file generated for article ID: 3416
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% Generated on 2025-12-18 11:45:42
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@article{ref_186051,
|
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author={N, DeLight and H, Sachs},
|
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title={Pneumoconiosis},
|
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year={2023},
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volume={2023}
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}
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|
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@article{ref_186052,
|
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author={Evaluation, Institute for Health Metrics and},
|
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title={Findings from the global burden of disease study 2017},
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year={2018},
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volume={2018}
|
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}
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|
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@article{ref_186053,
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author={XM, Qi and Y, Luo and MY, Song and al, et},
|
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title={Pneumoconiosis: current status and future prospects},
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journal={Chin Med J (Engl)},
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year={2021},
|
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volume={134},
|
||||
number={8},
|
||||
pages={898},
|
||||
doi={10.1097/CM9.0000000000001461}
|
||||
}
|
||||
|
||||
@article{ref_186054,
|
||||
author={QT, Pham},
|
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title={Chest radiography in the diagnosis of pneumoconiosis},
|
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|
||||
}
|
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|
||||
@article{ref_186055,
|
||||
author={Organization, International Labour},
|
||||
title={ILO International Classification of Radiographs of Pneumoconioses (2023)},
|
||||
year={2023},
|
||||
volume={21},
|
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pages={February}
|
||||
}
|
||||
|
||||
@article{ref_186056,
|
||||
author={EH, Shortliffe and JJ, Cimino and eds},
|
||||
title={Biomedical Informatics},
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journal={Springer International Publishing},
|
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|
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volume={2021},
|
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doi={10.1007/978-3-030-58721-5}
|
||||
}
|
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|
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@article{ref_186057,
|
||||
author={E, Alpaydin},
|
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title={Introduction to Machine Learning. 4th Edition},
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year={2020},
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volume={2020}
|
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}
|
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|
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@article{ref_186058,
|
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author={Y, LeCun and Y, Bengio and G, Hinton},
|
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title={Deep learning},
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journal={Nature},
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}
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@article{ref_186059,
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author={J, Deng and W, Dong and R, Socher and LJ, Li and Li, Kai and FF, Li},
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}
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@article{ref_186060,
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author={A, Paszke and A, Chaurasia and S, Kim and E, Culurciello},
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title={ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation},
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}
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@article{ref_186061,
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author={S, Sharma and K, Guleria},
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title={A Deep Learning based model for the Detection of Pneumonia from Chest X-Ray Images using VGG-16 and Neural Networks},
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journal={Proc Comput Sci},
|
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year={2023},
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doi={10.1016/j.procs.2023.01.018}
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}
|
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|
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@article{ref_186062,
|
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author={R, Kundu and R, Das and ZW, Geem and GT, Han and R, Sarkar},
|
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title={Pneumonia detection in chest X-ray images using an ensemble of deep learning models},
|
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journal={PLOS One},
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year={2021},
|
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volume={16},
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number={9},
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pages={e0256630},
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doi={10.1371/journal.pone.0256630}
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}
|
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|
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@article{ref_186063,
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author={F, Yang and ZR, Tang and J, Chen and al, et},
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title={Pneumoconiosis computer aided diagnosis system based on X-rays and deep learning},
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journal={BMC Med Imaging},
|
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year={2021},
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doi={10.1186/s12880-021-00723-z}
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}
|
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|
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@article{ref_186064,
|
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author={L, Devnath and S, Luo and P, Summons and al, et},
|
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title={Deep Ensemble Learning for the Automatic Detection of Pneumoconiosis in Coal Worker’s Chest X-ray Radiography},
|
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journal={JCM},
|
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year={2022},
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volume={11},
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number={18},
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pages={5342},
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doi={10.3390/jcm11185342}
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}
|
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|
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@article{ref_186065,
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author={E, Okumura and I, Kawashita and T, Ishida},
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title={Computerized Classification of Pneumoconiosis on Digital Chest Radiography Artificial Neural Network with Three Stages},
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year={2017},
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}
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|
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@article{ref_186066,
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author={Y, Arzhaeva and D, Wang and L, Devnath and al, et},
|
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title={Development of Automated Diagnostic Tools for Pneumoconiosis Detection from Chest X-Ray Radiographs},
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journal={Coal Services Health and Safety Trust Project No. 20647},
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volume={192938}
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}
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@article{ref_186067,
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author={M, Akgün and I, Ozmen and E, Ozari Yildirim and al, et},
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title={Pitfalls of using the ILO classification for silicosis compensation claims},
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journal={Occup Med},
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year={2022},
|
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volume={72},
|
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number={6},
|
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pages={372},
|
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doi={10.1093/occmed/kqac010}
|
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}
|
||||
|
||||
@article{ref_186068,
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author={NA, JP and N, Suganuma},
|
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title={Quality assurance in reading radiographs for pneumoconiosis: AIR Pneumo program},
|
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journal={ASEAN J Radiol},
|
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year={2020},
|
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volume={21},
|
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number={1},
|
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pages={73},
|
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doi={10.46475/aseanjr.v21i1.61}
|
||||
}
|
||||
|
||||
@article{ref_186069,
|
||||
author={Office, International Labour},
|
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title={Guidelines for the use of the ILO International Classification of Radiographs of Pneumoconioses Revised edition 2011},
|
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year={2011},
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volume={2011}
|
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}
|
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|
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@article{ref_186070,
|
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author={N, Newra},
|
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title={Lung Mask Image Dataset},
|
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journal={kaggle},
|
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year={2022},
|
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volume={2022}
|
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}
|
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|
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@article{ref_186071,
|
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author={S, Candemir and S, Jaeger and K, Palaniappan and al, et},
|
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title={Lung Segmentation in Chest Radiographs Using Anatomical Atlases With Nonrigid Registration},
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journal={IEEE Trans Med Imaging},
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year={2014},
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}
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|
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@article{ref_186072,
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author={S, Jaeger and A, Karargyris and S, Candemir and al, et},
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title={Automatic Tuberculosis Screening Using Chest Radiographs},
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}
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@article{ref_186073,
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title={U-Net: Convolutional Networks for Biomedical Image Segmentation},
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journal={Lecture Notes in Computer Science},
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year={2015},
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volume={2015},
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}
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@article{ref_186074,
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author={J, Long and E, Shelhamer and T, Darrell},
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title={Fully convolutional networks for semantic segmentation},
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journal={IEEE Trans Pattern Anal Mach Intell},
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doi={10.1109/CVPR.2015.7298965}
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}
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@article{ref_186075,
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author={K, He and G, Gkioxari and P, Dollar and R, Girshick},
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title={Mask R-CNN},
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journal={2017 IEEE International Conference on Computer Vision (ICCV)},
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year={2017},
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volume={2017},
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pages={2980},
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doi={10.1109/ICCV.2017.322}
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}
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@article{ref_186076,
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author={L-C, Chen and G, Papandreou and I, Kokkinos and K, Murphy and AL, Yuille},
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title={DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs},
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journal={IEEE Trans Pattern Anal Mach Intell},
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year={2018},
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number={4},
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doi={10.1109/TPAMI.2017.2699184}
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}
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|
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@article{ref_186077,
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author={M, Romero and Y, Interian and T, Solberg and G, Valdes},
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title={Targeted transfer learning to improve performance in small medical physics datasets},
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journal={Med Phys},
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year={2020},
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volume={47},
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number={12},
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doi={10.1002/mp.14507}
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}
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@article{ref_186078,
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author={K, He and X, Zhang and S, Ren and J, Sun},
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title={Deep Residual Learning for Image Recognition},
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journal={2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
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year={2016},
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volume={2016},
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}
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@article{ref_186079,
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title={Densely Connected Convolutional Networks},
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volume={2016},
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}
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@article{ref_186080,
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title={Very Deep Convolutional Networks for Large-Scale Visual Recognition},
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journal={International Conference on Learning Representations},
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volume={2015},
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}
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@article{ref_186081,
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title={Going deeper with convolutions},
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journal={2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
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}
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title={MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications},
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title={CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning},
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|
||||
author={Bayer-Santos, Ethel and Aguilar-Bonavides, Clemente and Rodrigues, Silas Pessini and Cordero, Esteban Maurício and Marques, Alexandre Ferreira and Varela-Ramirez, Armando and Choi, Hyungwon and Yoshida, Nobuko and Da Silveira, José Franco and Almeida, Igor C},
|
||||
journal={Journal of Proteome Research},
|
||||
volume={12},
|
||||
number={2},
|
||||
pages={883-897},
|
||||
year={2013},
|
||||
% publisher={ACS Publications},
|
||||
doi={10.1021/pr300948z}
|
||||
}
|
||||
Reference in New Issue
Block a user