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Deep Learning-based Approach for the Semantic Segmentation of Bright Retinal Damage

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Deep Learning-based Approach for the Semantic Segmentation of Bright Retinal Damage

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Silva, C.; Colomer, A.; Naranjo Ornedo, V. (2018). Deep Learning-based Approach for the Semantic Segmentation of Bright Retinal Damage. En Intelligent Data Engineering and Automated Learning – IDEAL 2018. Springer. 164-173. https://doi.org/10.1007/978-3-030-03493-1_18

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/124075

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Title: Deep Learning-based Approach for the Semantic Segmentation of Bright Retinal Damage
Author:
Issued date:
Abstract:
Regular screening for the development of diabetic retinopathy is imperative for an early diagnosis and a timely treatment, thus preventing further progression of the disease. The conventional screening techniques based ...[+]
Subjects: Semantic segmentation , Deep learning , Fundus images , Exudates , U-Net
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_18
Publisher:
Springer
Publisher version: http://dx.doi.org/10.1007/978-3-030-03493-1_18
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:
This paper was supported by the European Union’s Horizon 2020 research and innovation programme under the Project GALAHAD [H2020-ICT2016-2017, 732613]. The work of Adri´an Colomer has been supported by the Spanish Government ...[+]
Type: Capítulo de libro Comunicación en congreso

References

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Sopharak, A.: Machine learning approach to automatic exudate detection in retinal images from diabetic patients. J. Mod. Opt. 57(2), 124–135 (2010) [+]
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