Hybrid Deep Learning Gaussian Process for Diabetic Retinopathy Diagnosis and Uncertainty Quantification

Santiago Toledo-Cortés; Melissa de la Pava; Oscar Julian Perdomo Charry; Fabio A. González

doi:10.1007/978-3-030-63419-3_21

Hybrid Deep Learning Gaussian Process for Diabetic Retinopathy Diagnosis and Uncertainty Quantification

Santiago Toledo-Cortés
, Melissa de la Pava
, Oscar Julian Perdomo Charry
, Fabio A. González

Producción científica: Capítulo en Libro/Informe › Capítulo (revisado por pares) › revisión exhaustiva

19 Citas (Scopus)

Resumen

Diabetic Retinopathy (DR) is one of the microvascular complications of Diabetes Mellitus, which remains as one of the leading causes of blindness worldwide. Computational models based on Convolutional Neural Networks represent the state of the art for the automatic detection of DR using eye fundus images. Most of the current work address this problem as a binary classification task. However, including the grade estimation and quantification of predictions uncertainty can potentially increase the robustness of the model. In this paper, a hybrid Deep Learning-Gaussian process method for DR diagnosis and uncertainty quantification is presented. This method combines the representational power of deep learning, with the ability to generalize from small datasets of Gaussian process models

Idioma original	Inglés estadounidense
Título de la publicación alojada	Ophthalmic Medical Image Analysis - 7th International Workshop, OMIA 2020, Held in Conjunction with MICCAI 2020, Proceedings
Editores	Huazhu Fu, Mona K. Garvin, Tom MacGillivray, Yanwu Xu, Yalin Zheng
Editorial	Springer Science and Business Media Deutschland GmbH
Páginas	206-215
Número de páginas	10
ISBN (versión digital)	978-3-030-63419-3
ISBN (versión impresa)	978-3-030-63418-6
DOI	https://doi.org/10.1007/978-3-030-63419-3_21
Estado	Publicada - nov. 20 2020
Evento	6th International Workshop on Ophthalmic Medical Image Analysis, OMIA 2020, held in conjunction with 23rd International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2020 - Lima, Perú Duración: oct. 8 2020 → oct. 8 2020

Serie de la publicación

Nombre	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Editorial	Springer
Volumen	12069 LNCS
ISSN (versión impresa)	0302-9743
ISSN (versión digital)	1611-3349

Conferencia

Conferencia	6th International Workshop on Ophthalmic Medical Image Analysis, OMIA 2020, held in conjunction with 23rd International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2020
País/Territorio	Perú
Ciudad	Lima
Período	10/8/20 → 10/8/20

ODS de las Naciones Unidas

Este resultado contribuye a los siguientes Objetivos de Desarrollo Sostenible

ODS 3: Salud y bienestar

Áreas temáticas de ASJC Scopus

Ciencia computacional teórica
Ciencia de la Computación General

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10.1007/978-3-030-63419-3_21

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Toledo-Cortés, S., de la Pava, M., Perdomo Charry, O. J., & González, F. A. (2020). Hybrid Deep Learning Gaussian Process for Diabetic Retinopathy Diagnosis and Uncertainty Quantification. En H. Fu, M. K. Garvin, T. MacGillivray, Y. Xu, & Y. Zheng (Eds.), Ophthalmic Medical Image Analysis - 7th International Workshop, OMIA 2020, Held in Conjunction with MICCAI 2020, Proceedings (pp. 206-215). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12069 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-63419-3_21

Toledo-Cortés, Santiago ; de la Pava, Melissa ; Perdomo Charry, Oscar Julian et al. / Hybrid Deep Learning Gaussian Process for Diabetic Retinopathy Diagnosis and Uncertainty Quantification. Ophthalmic Medical Image Analysis - 7th International Workshop, OMIA 2020, Held in Conjunction with MICCAI 2020, Proceedings. editor / Huazhu Fu ; Mona K. Garvin ; Tom MacGillivray ; Yanwu Xu ; Yalin Zheng. Springer Science and Business Media Deutschland GmbH, 2020. pp. 206-215 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Hybrid Deep Learning Gaussian Process for Diabetic Retinopathy Diagnosis and Uncertainty Quantification",

abstract = "Diabetic Retinopathy (DR) is one of the microvascular complications of Diabetes Mellitus, which remains as one of the leading causes of blindness worldwide. Computational models based on Convolutional Neural Networks represent the state of the art for the automatic detection of DR using eye fundus images. Most of the current work address this problem as a binary classification task. However, including the grade estimation and quantification of predictions uncertainty can potentially increase the robustness of the model. In this paper, a hybrid Deep Learning-Gaussian process method for DR diagnosis and uncertainty quantification is presented. This method combines the representational power of deep learning, with the ability to generalize from small datasets of Gaussian process models",

author = "Santiago Toledo-Cort{\'e}s and \{de la Pava\}, Melissa and \{Perdomo Charry\}, \{Oscar Julian\} and Gonz{\'a}lez, \{Fabio A.\}",

note = "Funding Information: Acknowledgments. This work was partially supported by a Google Research Award and by the Colciencias project number 1101-807-63563. Publisher Copyright: {\textcopyright} 2020, Springer Nature Switzerland AG. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.; 6th International Workshop on Ophthalmic Medical Image Analysis, OMIA 2020, held in conjunction with 23rd International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2020 ; Conference date: 08-10-2020 Through 08-10-2020",

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Toledo-Cortés, S, de la Pava, M, Perdomo Charry, OJ & González, FA 2020, Hybrid Deep Learning Gaussian Process for Diabetic Retinopathy Diagnosis and Uncertainty Quantification. En H Fu, MK Garvin, T MacGillivray, Y Xu & Y Zheng (eds.), Ophthalmic Medical Image Analysis - 7th International Workshop, OMIA 2020, Held in Conjunction with MICCAI 2020, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12069 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 206-215, 6th International Workshop on Ophthalmic Medical Image Analysis, OMIA 2020, held in conjunction with 23rd International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2020, Lima, Perú, 10/8/20. https://doi.org/10.1007/978-3-030-63419-3_21

Hybrid Deep Learning Gaussian Process for Diabetic Retinopathy Diagnosis and Uncertainty Quantification. / Toledo-Cortés, Santiago; de la Pava, Melissa; Perdomo Charry, Oscar Julian et al.
Ophthalmic Medical Image Analysis - 7th International Workshop, OMIA 2020, Held in Conjunction with MICCAI 2020, Proceedings. ed. / Huazhu Fu; Mona K. Garvin; Tom MacGillivray; Yanwu Xu; Yalin Zheng. Springer Science and Business Media Deutschland GmbH, 2020. p. 206-215 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12069 LNCS).

Producción científica: Capítulo en Libro/Informe › Capítulo (revisado por pares) › revisión exhaustiva

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AU - Toledo-Cortés, Santiago

AU - de la Pava, Melissa

AU - Perdomo Charry, Oscar Julian

AU - González, Fabio A.

N1 - Funding Information: Acknowledgments. This work was partially supported by a Google Research Award and by the Colciencias project number 1101-807-63563. Publisher Copyright: © 2020, Springer Nature Switzerland AG. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.

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Y1 - 2020/11/20

N2 - Diabetic Retinopathy (DR) is one of the microvascular complications of Diabetes Mellitus, which remains as one of the leading causes of blindness worldwide. Computational models based on Convolutional Neural Networks represent the state of the art for the automatic detection of DR using eye fundus images. Most of the current work address this problem as a binary classification task. However, including the grade estimation and quantification of predictions uncertainty can potentially increase the robustness of the model. In this paper, a hybrid Deep Learning-Gaussian process method for DR diagnosis and uncertainty quantification is presented. This method combines the representational power of deep learning, with the ability to generalize from small datasets of Gaussian process models

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A2 - Fu, Huazhu

A2 - Garvin, Mona K.

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Toledo-Cortés S, de la Pava M, Perdomo Charry OJ, González FA. Hybrid Deep Learning Gaussian Process for Diabetic Retinopathy Diagnosis and Uncertainty Quantification. En Fu H, Garvin MK, MacGillivray T, Xu Y, Zheng Y, editores, Ophthalmic Medical Image Analysis - 7th International Workshop, OMIA 2020, Held in Conjunction with MICCAI 2020, Proceedings. Springer Science and Business Media Deutschland GmbH. 2020. p. 206-215. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-63419-3_21

Hybrid Deep Learning Gaussian Process for Diabetic Retinopathy Diagnosis and Uncertainty Quantification

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Áreas temáticas de ASJC Scopus

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