A Coarse-to-Fine Segmentation Methodology Based on Deep Networks for Automated Analysis of Cryptosporidium Parasite from Fluorescence Microscopic Images
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-16961-8_16
Reference31 articles.
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3. Hatam-Nahavandi, K., Ahmadpour, E., Carmena, D., Spotin, A., Bangoura, B., Xiao, L.: Cryptosporidium infections in terrestrial ungulates with focus on livestock: a systematic review and meta-analysis. Parasit. Vectors 12(1), 1–23 (2019)
4. Gerace, E., Presti, V.D.M.L., Biondo, C.: Cryptosporidium infection: epidemiology, pathogenesis, and differential diagnosis. Eur. J. Microbiol. Immunol. 9(4), 119–123 (2019)
5. Kotloff, K.L., et al.: Burden and aetiology of diarrhoeal disease in infants and young children in developing countries (the global enteric multicenter study, GEMs): a prospective, case-control study. The Lancet 382(9888), 209–222 (2013)
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