Standard
Deep-Learning-Based Classification of Rat OCT Images After Intravitreal Injection of ET-1 for Glaucoma Understanding. / Fuentes-Hurtado, Félix; Morales, Sandra; Mossi, Jose M.; Naranjo, Valery; Fedulov, Vadim; Woldbye, David; Klemp, Kristian; Torm, Marie; Larsen, Michael.
Intelligent Data Engineering and Automated Learning – IDEAL 2018 - 19th International Conference, Proceedings. ed. / Hujun Yin; Paulo Novais; David Camacho; Antonio J. Tallón-Ballesteros. Springer, 2018. p. 27-34 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 11314 LNCS).
Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
Harvard
Fuentes-Hurtado, F, Morales, S, Mossi, JM, Naranjo, V, Fedulov, V
, Woldbye, D, Klemp, K, Torm, M & Larsen, M 2018,
Deep-Learning-Based Classification of Rat OCT Images After Intravitreal Injection of ET-1 for Glaucoma Understanding. in H Yin, P Novais, D Camacho & AJ Tallón-Ballesteros (eds),
Intelligent Data Engineering and Automated Learning – IDEAL 2018 - 19th International Conference, Proceedings. Springer, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11314 LNCS, pp. 27-34, 19th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2018, Madrid, Spain,
21/11/2018.
https://doi.org/10.1007/978-3-030-03493-1_4APA
Fuentes-Hurtado, F., Morales, S., Mossi, J. M., Naranjo, V., Fedulov, V.
, Woldbye, D., Klemp, K., Torm, M., & Larsen, M. (2018).
Deep-Learning-Based Classification of Rat OCT Images After Intravitreal Injection of ET-1 for Glaucoma Understanding. In H. Yin, P. Novais, D. Camacho, & A. J. Tallón-Ballesteros (Eds.),
Intelligent Data Engineering and Automated Learning – IDEAL 2018 - 19th International Conference, Proceedings (pp. 27-34). Springer. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 11314 LNCS
https://doi.org/10.1007/978-3-030-03493-1_4Vancouver
Fuentes-Hurtado F, Morales S, Mossi JM, Naranjo V, Fedulov V
, Woldbye D et al.
Deep-Learning-Based Classification of Rat OCT Images After Intravitreal Injection of ET-1 for Glaucoma Understanding. In Yin H, Novais P, Camacho D, Tallón-Ballesteros AJ, editors, Intelligent Data Engineering and Automated Learning – IDEAL 2018 - 19th International Conference, Proceedings. Springer. 2018. p. 27-34. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 11314 LNCS).
https://doi.org/10.1007/978-3-030-03493-1_4Author
Fuentes-Hurtado, Félix ; Morales, Sandra ; Mossi, Jose M. ; Naranjo, Valery ; Fedulov, Vadim ; Woldbye, David ; Klemp, Kristian ; Torm, Marie ; Larsen, Michael. / Deep-Learning-Based Classification of Rat OCT Images After Intravitreal Injection of ET-1 for Glaucoma Understanding. Intelligent Data Engineering and Automated Learning – IDEAL 2018 - 19th International Conference, Proceedings. editor / Hujun Yin ; Paulo Novais ; David Camacho ; Antonio J. Tallón-Ballesteros. Springer, 2018. pp. 27-34 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 11314 LNCS).
Bibtex
@inproceedings{01756d4a3f344843b984b544acb2a3fa,
title = "Deep-Learning-Based Classification of Rat OCT Images After Intravitreal Injection of ET-1 for Glaucoma Understanding",
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 injection of Endothelin-1, respectively) making use of the DenseNet-201 architecture fine-tuned and a customized top-model. We validated the performance of the method on 1912 OCT images yielding promising results in a leave-P-out cross-validation). Besides, we also compared the results of the fine-tuned network with those achieved training the network from scratch, obtaining some interesting insights. The presented method poses a step forward in understanding pathological rodent OCT retinal images, as at the moment there is no known discriminating characteristic which allows classifying this type of images accurately. The result of this work is a very accurate and robust automatic method to distinguish between healthy and a rodent model of glaucoma, which is the backbone of future works dealing with human OCT images.",
keywords = "Deep-learning, Glaucoma, Optical coherence tomography",
author = "F{\'e}lix Fuentes-Hurtado and Sandra Morales and Mossi, {Jose M.} and Valery Naranjo and Vadim Fedulov and David Woldbye and Kristian Klemp and Marie Torm and Michael Larsen",
year = "2018",
doi = "10.1007/978-3-030-03493-1_4",
language = "English",
isbn = "9783030034924",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer",
pages = "27--34",
editor = "Hujun Yin and Paulo Novais and David Camacho and Tall{\'o}n-Ballesteros, {Antonio J.}",
booktitle = "Intelligent Data Engineering and Automated Learning – IDEAL 2018 - 19th International Conference, Proceedings",
address = "Switzerland",
note = "19th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2018 ; Conference date: 21-11-2018 Through 23-11-2018",
}
RIS
TY - GEN
T1 - Deep-Learning-Based Classification of Rat OCT Images After Intravitreal Injection of ET-1 for Glaucoma Understanding
AU - Fuentes-Hurtado, Félix
AU - Morales, Sandra
AU - Mossi, Jose M.
AU - Naranjo, Valery
AU - Fedulov, Vadim
AU - Woldbye, David
AU - Klemp, Kristian
AU - Torm, Marie
AU - Larsen, Michael
PY - 2018
Y1 - 2018
N2 - 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 injection of Endothelin-1, respectively) making use of the DenseNet-201 architecture fine-tuned and a customized top-model. We validated the performance of the method on 1912 OCT images yielding promising results in a leave-P-out cross-validation). Besides, we also compared the results of the fine-tuned network with those achieved training the network from scratch, obtaining some interesting insights. The presented method poses a step forward in understanding pathological rodent OCT retinal images, as at the moment there is no known discriminating characteristic which allows classifying this type of images accurately. The result of this work is a very accurate and robust automatic method to distinguish between healthy and a rodent model of glaucoma, which is the backbone of future works dealing with human OCT images.
AB - 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 injection of Endothelin-1, respectively) making use of the DenseNet-201 architecture fine-tuned and a customized top-model. We validated the performance of the method on 1912 OCT images yielding promising results in a leave-P-out cross-validation). Besides, we also compared the results of the fine-tuned network with those achieved training the network from scratch, obtaining some interesting insights. The presented method poses a step forward in understanding pathological rodent OCT retinal images, as at the moment there is no known discriminating characteristic which allows classifying this type of images accurately. The result of this work is a very accurate and robust automatic method to distinguish between healthy and a rodent model of glaucoma, which is the backbone of future works dealing with human OCT images.
KW - Deep-learning
KW - Glaucoma
KW - Optical coherence tomography
U2 - 10.1007/978-3-030-03493-1_4
DO - 10.1007/978-3-030-03493-1_4
M3 - Article in proceedings
AN - SCOPUS:85057091948
SN - 9783030034924
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 27
EP - 34
BT - Intelligent Data Engineering and Automated Learning – IDEAL 2018 - 19th International Conference, Proceedings
A2 - Yin, Hujun
A2 - Novais, Paulo
A2 - Camacho, David
A2 - Tallón-Ballesteros, Antonio J.
PB - Springer
T2 - 19th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2018
Y2 - 21 November 2018 through 23 November 2018
ER -
