Study on Fine-Grained Visual Classification of Low-Resolution Urinary Erythrocyte
Author:
Funder
National Natural Science Foundation of China
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s10278-024-01082-1.pdf
Reference34 articles.
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2. Zhang S. Research on automatic detection of urine sediments based on deep learning. Anhui: Anhui University, 2020.
3. Xie X M, Zhang J F, Zhong M X, Lv J Z, Chen J Y. The value of urine erythrocyte morphological typing in the differential diagnosis of renal isohematuria. Journal of Practical Medical Technology 2020,25(02):175-1176.https://doi.org/10.19522/j.cnki.1671-5098.2018.02.026
4. Ran H. The diagnostic significance of urine erythrocyte morphology in hematuria localization test. Electronic Journal of Clinical Medicine Literature 2017,4(04):623. https://doi.org/10.16281/j.cnki.jocml.2017.04.020.
5. Zheng X. Clinical effect of microscopic examination and automatic analyzer for urine sediment in routine urine test. Journal of Clinical Medicine Literature 2019, 8(01):38-40.
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