StoneNet: An Efficient Lightweight Model Based on Depthwise Separable Convolutions for Kidney Stone Detection from CT Images
Author:
Publisher
Springer Science and Business Media LLC
Subject
Health Informatics,Computer Science Applications,General Biochemistry, Genetics and Molecular Biology
Link
https://link.springer.com/content/pdf/10.1007/s12539-023-00578-8.pdf
Reference39 articles.
1. Vupputuri S, Soucie JM, McClellan W, Sandler DP (2004) History of kidney stones as a possible risk factor for chronic kidney disease. Ann Epidemiol 14(3):222–228. https://doi.org/10.1016/S1047-2797(03)00126-1
2. Edvardsson VO, Indridason OS, Haraldsson G, Kjartansson O, Palsson R (2013) Temporal trends in the incidence of kidney stone disease. Kidney Int 83(1):146–152. https://doi.org/10.1038/ki.2012.320
3. Sorokin I, Mamoulakis C, Miyazawa K, Rodgers A, Talati J, Lotan Y (2017) Epidemiology of stone disease across the world. World J Urol 35(9):1301–1320. https://doi.org/10.1007/s00345-017-2008-6
4. Park J, Kang JB, Chang JH, Yoo Y (2014) Speckle reduction techniques in medical ultrasound imaging. Biomed Eng Lett 4(1):32–40. https://doi.org/10.1007/s13534-014-0122-6
5. Shlipak MG, Fried LF, Cushman M, Manolio TA, Peterson D, Stehman-Breen C et al (2005) Cardiovascular mortality risk in chronic kidney disease: comparison of traditional and novel risk factors. JAMA 293(14):1737–1745. https://doi.org/10.1001/jama.293.14.1737
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