Classification of neck tissues in OCT images by using convolutional neural network
Author:
Funder
National Natural Science Foundation of China
Beijing-Tianjin-Hebei Basic Research Cooperation Special Program
Science and Technology Support Program of Tianjin
Publisher
Springer Science and Business Media LLC
Subject
Dermatology,Surgery
Link
https://link.springer.com/content/pdf/10.1007/s10103-022-03665-2.pdf
Reference25 articles.
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3. Takusagawa HL, Hoguet A, Junk AK, Nouri-Mandavi K, Radhakrishnan S, Chen TC (2019) Swept-source OCT for evaluating the lamina cribrosa: a report by the American academy of ophthalmology. Ophthalmology 126(9):1315–1323. https://doi.org/10.1016/j.ophtha.2019.03.044
4. Tsai TH, Leggett CL, Trindade AJ, Sethi A, Swager A-F, Joshi V, Bergman JJ, Mashimo H, Nishioka NS, Namati E (2017) Optical coherence tomography in gastroenterology: a review and future outlook. J Biomed Opt 22(12):121716. https://doi.org/10.1117/1.jbo.22.12.121716
5. de Boer JF, Leitgeb R, Wojtkowski M (2017) Twenty-five years of optical coherence tomography: the paradigm shift in sensitivity and speed provided by Fourier domain OCT. Biomed Opt Express 8(7):3248–3280. https://doi.org/10.1364/BOE.8.003248
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