Jiang Y, Yang M, Wang S, Li X, Sun Y. Emerging role of deep learning-based artificial intelligence in tumor pathology. Cancer Commun [Internet]. 2020;40:154–66. https://doi.org/10.1002/cac2.12012.
Durmuş MA, Kömeç S, Gülmez A. Artificial intelligence applications for immunology laboratory: image analysis and classification study of IIF photos. Immunol Res [Internet]. 2024;72:1277–87. https://doi.org/10.1007/s12026-024-09527-z.
MacMath D, Chen M, Khoury P. Artificial intelligence: exploring the future of innovation in allergy immunology. Curr Allergy Asthma Rep. 2023;23:351–62.
Försch S, Klauschen F, Hufnagl P, Roth W. Artificial intelligence in pathology. Dtsch Arztebl Int. Deutscher Arzte-Verlag GmbH. 2021;199–204.
Microsoft. How to improve your Custom Vision model [Internet]. Microsoft Learn. 2025. Available from: https://learn.microsoft.com/en-us/azure/ai-services/custom-vision-service/getting-started-improving-your-classifier. Accessed 29 Mar 2025.
Voigt J, Krause C, Rohwäder E, Saschenbrecker S, Hahn M, Danckwardt M, et al. Automated indirect immunofluorescence evaluation of antinuclear autoantibodies on HEp-2 cells. J Immunol Res. 2012;2012:651058.
Park Y, Kim SY, Kwon GC, Koo SH, Kang E-S, Kim J. Automated versus conventional microscopic interpretation of antinuclear antibody indirect immunofluorescence test. Ann Clin Lab Sci. 2019;49:127–33.
Gorgi Y, Dhaouadi T, Sfar I, Haouami Y, Ben AT, Raso G, et al. Comparative study of human and automated screening for antinuclear antibodies by immunofluorescence on HEp-2 cells. Int J Stat Med Res. 2015;4:270–6.
Hobson P, Lovell BC, Percannella G, Vento M, Wiliem A. Benchmarking human epithelial type 2 interphase cells classification methods on a very large dataset. Artif Intell Med. 2015;65:239–50.
Boral B, Togay A. Automatic classification of antinuclear antibody patterns with machine learning. Cureus [Internet]. 2023;15:e45008. https://doi.org/10.7759/cureus.45008
Bizzaro N, Antico A, Platzgummer S, Tonutti E, Bassetti D, Pesente F, et al. Automated antinuclear immunofluorescence antibody screening: a comparative study of six computer-aided diagnostic systems. Autoimmun Rev. 2014;13:292–8.
Article CAS PubMed Google Scholar
Rodrigues LF, Naldi MC, Mari JF. Comparing convolutional neural networks and preprocessing techniques for HEp-2 cell classification in immunofluorescence images. Comput Biol Med. 2020;116:103542.
Article CAS PubMed Google Scholar
Cascio D, Taormina V, Raso G. Deep convolutional neural network for HEp-2 fluorescence intensity classification. Appl Sci. 2019;9:408.
Cascio D, Taormina V, Raso G. Deep CNN for IIF images classification in autoimmune diagnostics. Appl Sci. 2019;9:408.
Bayramoglu N, Kannala J, Heikkila J. Human epithelial type 2 cell classification with convolutional neural networks. 2015 IEEE 15th International Conference on Bioinformatics and Bioengineering (BIBE). IEEE; 2015. p. 1–6.
Rahman S, Wang L, Sun C, Zhou L. Deep learning based HEp-2 image classification: a comprehensive review. Med Image Anal. 2020;65: 101764.
Tebo AE. Recent approaches to optimize laboratory assessment of antinuclear antibodies. Clin Vaccine Immunol. 2017;24:e00270-e317.
Article CAS PubMed PubMed Central Google Scholar
Yoo IY, Oh JW, Cha H-S, Koh E-M, Kang E-S. Performance of an automated fluorescence antinuclear antibody image analyzer. Ann Lab Med. 2017;37:240–7.
Article CAS PubMed PubMed Central Google Scholar
van Beers JJBC, Hahn M, Fraune J, Mallet K, Krause C, Hormann W, et al. Performance analysis of automated evaluation of antinuclear antibody indirect immunofluorescent tests in a routine setting. Autoimmunity Highlights [Internet]. 2018;9:8. https://doi.org/10.1007/s13317-018-0108-y.
Article CAS PubMed Google Scholar
Van Hoovels L, Schouwers S, Van den Bremt S, Bossuyt X. Variation in antinuclear antibody detection by automated indirect immunofluorescence analysis. Ann Rheum Dis. 2019;78:e48–e48.
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