Deep Learning Models of Multi-Scale Lesion Perception Attention Networks for Diagnosis and Staging of Pneumoconiosis: A Comparative Study with Radiologists
Author:
Funder
Hefei Independent Innovation Policy “Loan to Supplement” Fund Project
Hefei Academic and Technical Leader and Backup Candidate Scientific Research Project
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s10278-024-01125-7.pdf
Reference33 articles.
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2. Wang D, Liang R, Yang M, Ma J, Li W, Mu M, Xiao Y, Feng X, Dong C, Yu L, Liu W, Tan Q, Zhou M, Wang B, Shi T, Yuan L, Chen W: Incidence and disease burden of coal workers’ pneumoconiosis worldwide, 1990–2019: evidence from the Global Burden of Disease Study 2019. Eur Respir J 58:2101669, 2021
3. Jun JS, Jung JI, Kim HR, Ahn MI, Han DH, Ko JM, Park SH, Lee HG, Arakawa H, Koo J: Complications of pneumoconiosis: Radiologic overview. Eur J Radiol 82:1819-1830, 2013
4. Zhao H, Xie Y, Wang J, Li X, Li J: Pulmonary rehabilitation for pneumoconiosis: protocol for a systematic review and meta-analysis. Bmj Open 9:e25891, 2019
5. Wang N, Yin Q, Du X: Experimental Investigation into the Performances of Water Adsorption in Kaolinite Clay: Implications for Occupational Pneumoconiosis Prevention and Treatment. J Taibah Univ Sci 16:535-549, 2022
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