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Deep-Learning-based Classification of Rat OCT images After Intravitreal Injection of ET-1 for Glaucoma Understanding

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Deep-Learning-based Classification of Rat OCT images After Intravitreal Injection of ET-1 for Glaucoma Understanding

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Fuentes-Hurtado, FJ.; Morales, S.; Mossi García, JM.; Naranjo Ornedo, V.; Fedulov, V.; Woldbye, D.; Klemp, K.... (2018). Deep-Learning-based Classification of Rat OCT images After Intravitreal Injection of ET-1 for Glaucoma Understanding. En Intelligent Data Engineering and Automated Learning – IDEAL 2018. Springer. 27-34. https://doi.org/10.1007/978-3-030-03493-1_4

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/124182

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Title: Deep-Learning-based Classification of Rat OCT images After Intravitreal Injection of ET-1 for Glaucoma Understanding
Author:
UPV Unit: Universitat Politècnica de València. Departamento de Comunicaciones - Departament de Comunicacions
Issued date:
Abstract:
Optical coherence tomography (OCT) is a useful technique to monitor retinal damage. We present an automatic method to accurately classify rodent OCT images in healthy and pathological (before and after 14 days of intravitreal ...[+]
Subjects: Optical coherence tomography , Deep-learning , Glaucoma
Copyrigths: Reserva de todos los derechos
ISBN: 978-3-030-03492-4
Source:
Intelligent Data Engineering and Automated Learning – IDEAL 2018.
DOI: 10.1007/978-3-030-03493-1_4
Publisher:
Springer
Publisher version: https://doi.org/10.1007/978-3-030-03493-1_4
Conference name: International Conference on Intelligent Data Engineering and Automated Learning (IDEAL)
Conference place: Madrid, Spain
Conference date: Noviembre 21-23,2018
Series: Lecture Notes in Computer Science;11314
Project ID: info:eu-repo/grantAgreement/EC/H2020/732613/EU
Thanks:
Animal experiment permission was granted by the Danish Animal Experimentation Council (license number: 2017-15-0201-01213). We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan Xp GPU ...[+]
Type: Capítulo de libro Comunicación en congreso

References

Karri, S., Chakraborty, D., Chatterjee, J.: Transfer learning based classification of optical coherence tomography images with diabetic macular edema and dry age-related macular degeneration. Biomed. Opt. Express 8(2), 579–592 (2017)

Pekala, M., Joshi, N., Freund, D.E., Bressler, N.M., et al.: Deep learning based retinal OCT segmentation. arXiv preprint arXiv:1801.09749 (2018)

Srinivasan, P.P., Kim, L.A., Mettu, P.S., et al.: Fully automated detection of diabetic macular edema and dry age-related macular degeneration from optical coherence tomography images. Biomed. Opt. Express 5(10), 3568–3577 (2014) [+]
Karri, S., Chakraborty, D., Chatterjee, J.: Transfer learning based classification of optical coherence tomography images with diabetic macular edema and dry age-related macular degeneration. Biomed. Opt. Express 8(2), 579–592 (2017)

Pekala, M., Joshi, N., Freund, D.E., Bressler, N.M., et al.: Deep learning based retinal OCT segmentation. arXiv preprint arXiv:1801.09749 (2018)

Srinivasan, P.P., Kim, L.A., Mettu, P.S., et al.: Fully automated detection of diabetic macular edema and dry age-related macular degeneration from optical coherence tomography images. Biomed. Opt. Express 5(10), 3568–3577 (2014)

Lee, C.S., Baughman, D.M., Lee, A.Y.: Deep learning is effective for the classification of OCT images of normal versus age-related macular degeneration. arXiv preprint arXiv:1612.04891 (2016)

Muhammad, H., Fuchs, T.J., De Cuir, N., De Moraes, C.G., et al.: Hybrid deep learning on single wide-field optical coherence tomography scans accurately classifies glaucoma suspects. J. Glaucoma 26(12), 1086–1094 (2017)

Virgili, G., Michelessi, M., Cook, J., Boachie, C., et al.: Diagnostic accuracy of optical coherence tomography for diagnosing glaucoma: secondary analyses of the gate study. Br. J. Ophthalmol. 102(5), 604–610 (2017). bjophthalmol-2017

Hood, D.C.: Improving our understanding, and detection, of glaucomatous damage: an approach based upon OCT. Prog. Retin. eye res. 57, 46–75 (2017)

Nagata, A., Omachi, K., Higashide, T., et al.: OCT evaluation of neuroprotective effects of tafluprost on retinal injury after intravitreal injection of endothelin-1 in the rat eye. Invest. Ophthalmol. Vis. Sci. 55(2), 1040–1047 (2014)

Huang, G., Liu, Z., Weinberger, K.Q., van der Maaten, L.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, vol. 1, p. 3 (2017)

Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)

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