Hybrid Deep Learning Gaussian Process for Diabetic Retinopathy Diagnosis and Uncertainty Quantification
La retinopatía diabética (RD) es una de las complicaciones microvasculares de la diabetes mellitus, que sigue siendo una de las principales causas de ceguera en todo el mundo. Los modelos computacionales basados en redes neuronales convolucionales representan el estado del arte para la detección a...
- Autores:
-
Gonzalez Osorio, Fabio
Perdomo Charry, Oscar Julian
Toledo Cortes, Santiago
De La Pava, Melissa
- Tipo de recurso:
- Article of investigation
- Fecha de publicación:
- 2020
- Institución:
- Escuela Colombiana de Ingeniería Julio Garavito
- Repositorio:
- Repositorio Institucional ECI
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.escuelaing.edu.co:001/1425
- Acceso en línea:
- https://repositorio.escuelaing.edu.co/handle/001/1425
- Palabra clave:
- Retinopatía diabética
Aprendizaje
Método gaussiano
Gaussian method
Deep Learning
Diabetic Retinopathy
Gaussian Process
Uncertainty Quantification
Aprendizaje profundo
Retinopatía diabética
Proceso gaussiano
Cuantificación de la incertidumbre
- Rights
- openAccess
- License
- http://purl.org/coar/access_right/c_abf2
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| dc.title.spa.fl_str_mv |
Hybrid Deep Learning Gaussian Process for Diabetic Retinopathy Diagnosis and Uncertainty Quantification |
| title |
Hybrid Deep Learning Gaussian Process for Diabetic Retinopathy Diagnosis and Uncertainty Quantification |
| spellingShingle |
Hybrid Deep Learning Gaussian Process for Diabetic Retinopathy Diagnosis and Uncertainty Quantification Retinopatía diabética Aprendizaje Método gaussiano Gaussian method Deep Learning Diabetic Retinopathy Gaussian Process Uncertainty Quantification Aprendizaje profundo Retinopatía diabética Proceso gaussiano Cuantificación de la incertidumbre |
| title_short |
Hybrid Deep Learning Gaussian Process for Diabetic Retinopathy Diagnosis and Uncertainty Quantification |
| title_full |
Hybrid Deep Learning Gaussian Process for Diabetic Retinopathy Diagnosis and Uncertainty Quantification |
| title_fullStr |
Hybrid Deep Learning Gaussian Process for Diabetic Retinopathy Diagnosis and Uncertainty Quantification |
| title_full_unstemmed |
Hybrid Deep Learning Gaussian Process for Diabetic Retinopathy Diagnosis and Uncertainty Quantification |
| title_sort |
Hybrid Deep Learning Gaussian Process for Diabetic Retinopathy Diagnosis and Uncertainty Quantification |
| dc.creator.fl_str_mv |
Gonzalez Osorio, Fabio Perdomo Charry, Oscar Julian Toledo Cortes, Santiago De La Pava, Melissa |
| dc.contributor.author.none.fl_str_mv |
Gonzalez Osorio, Fabio Perdomo Charry, Oscar Julian Toledo Cortes, Santiago De La Pava, Melissa |
| dc.contributor.researchgroup.spa.fl_str_mv |
GiBiome |
| dc.subject.armarc.none.fl_str_mv |
Retinopatía diabética Aprendizaje |
| topic |
Retinopatía diabética Aprendizaje Método gaussiano Gaussian method Deep Learning Diabetic Retinopathy Gaussian Process Uncertainty Quantification Aprendizaje profundo Retinopatía diabética Proceso gaussiano Cuantificación de la incertidumbre |
| dc.subject.armarc.spa.fl_str_mv |
Método gaussiano |
| dc.subject.armarc.eng.fl_str_mv |
Gaussian method |
| dc.subject.proposal.spa.fl_str_mv |
Deep Learning Diabetic Retinopathy Gaussian Process Uncertainty Quantification Aprendizaje profundo Retinopatía diabética Proceso gaussiano Cuantificación de la incertidumbre |
| description |
La retinopatía diabética (RD) es una de las complicaciones microvasculares de la diabetes mellitus, que sigue siendo una de las principales causas de ceguera en todo el mundo. Los modelos computacionales basados en redes neuronales convolucionales representan el estado del arte para la detección automática de RD utilizando imágenes de fondo de ojo. La mayor parte del trabajo actual aborda este problema como una tarea de clasificación binaria. Sin embargo, incluir la estimación de leyes y la cuantificación de la incertidumbre de las predicciones puede aumentar potencialmente la solidez del modelo. En este artículo, se presenta un método de proceso híbrido de aprendizaje profundo y gaussiano para el diagnóstico de RD y la cuantificación de la incertidumbre. Este método combina el poder de representación del aprendizaje profundo con la capacidad de generalizar a partir de pequeños conjuntos de datos de modelos de procesos gaussianos. Los resultados muestran que la cuantificación de la incertidumbre en las predicciones mejora la interpretabilidad del método como herramienta de apoyo al diagnóstico |
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2020 |
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2020 |
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2021-05-12T19:00:05Z 2021-10-01T17:16:54Z |
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2021-05-12 2021-10-01T17:16:54Z |
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Artículo de revista |
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http://purl.org/coar/version/c_970fb48d4fbd8a85 |
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https://repositorio.escuelaing.edu.co/handle/001/1425 |
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10.1007/978-3-030-63419-3_21 |
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DOI:10.1007/978-3-030-63419-3_21 |
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0302-9743 10.1007/978-3-030-63419-3_21 DOI:10.1007/978-3-030-63419-3_21 |
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eng |
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Lecture Notes in Computer Science (LNCS, volumen 12069) |
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Abrámoff, M.D., et al.: Automated analysis of retinal images for detection of referable diabetic retinopathy. JAMA Ophthalmol. 131(3), 351–357 (2013). American Academy of Ophthalmology: International clinical diabetic retinopathy disease severity scale detailed table. International Council of Ophthalmology (2002) Bradshaw, J., Matthews, A.G.d.G., Ghahramani, Z.: Adversarial examples, uncertainty, and transfer testing robustness in Gaussian process hybrid deep networks, eprint, pp. 1-33 (2017). Decenciére, E., et al.: Feedback on a publicly distributed image database: the Messidor database. Image Anal. Stereol. 33(3), 231–234 (2014). Diabetic Retinopathy Detection of Kaggle: Eyepacs challenge. Ethem, A.: Introduction to Machine Learning, 3rd edn. The MIT Press, Cambridge (2014) Gargeya, R., Leng, T.: Automated identification of diabetic retinopathy using deep learning. Ophthalmology 124(7), 962–969 (2017) Gulshan, V., et al.: Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA J. Am. Med. Assoc. 316(22), 2402–2410 (2016) Kaya, M., Bilge, H.: Deep metric learning: a survey. Symmetry 11, 1066 (2019). Krause, J., et al.: Grader variability and the importance of reference standards for evaluating machine learning models for diabetic retinopathy. Ophthalmology 125(8), 1264–1272 (2018). Leibig, C., Allken, V., Ayhan, M.S., Berens, P., Wahl, S.: Leveraging uncertainty information from deep neural networks for disease detection. Sci. Rep. 7(1), 1–14 (2017) Li, F., Liu, Z., Chen, H., Jiang, M., Zhang, X., Wu, Z.: Automatic detection of diabetic retinopathy in retinal fundus photographs based on deep learning algorithm. Transl. Vis. Sci. Technol. 8(6) (2019). Lim, Z.W., Lee, M.L., Hsu, W., Wong, T.Y.: Building trust in deep learning system towards automated disease detection. In: The Thirty-First AAAI Conference on Innovative Applications of Artificial Intelligence, pp. 9516–9521 (2018) Perdomo, O., Gonzalez, F.: A systematic review of deep learning methods applied to ocular images. Ciencia e Ingenieria Neogranadina 30(1), 9–26 (2019) Raghu, M., Blumer, K., Sayres, R., Obermeyer, Z., Kleinberg, R., Mullainathan, S., Kleinberg, J.: Direct uncertainty prediction for medical second opinions. In: Proceedings of the 36th International Conference on Machine Learning, PMLR 97, Long Beach, California (2019) Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, December 2016, pp. 2818–2826 (2016). Voets, M., Møllersen, K., Bongo, L.A.: Reproduction study using public data of: Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. PLoS ONE 14(6), 1–11 (2019) Wells, J.A., et al.: aflibercept, bevacizumab, or ranibizumab for diabetic macular edema two-year results from a comparative effectiveness randomized clinical trial. Ophthalmology 123(6), 1351–1359 (2016) Wilkinson, C.P.P., et al.: Proposed international clinical diabetic retinopathy and diabetic macular edema disease severity scales. Ophthalmology 110(9), 1677–1682 (2003) Wilson, A., Nickisch, H.: Kernel interpolation for scalable structured gaussian processes (KISS-GP). In: Proceedings of the 32nd International Conference on Machine Learning. JMLR: W&CP, Lille, France (2015) Xin, Q., Elliot, M., Miikkulainen, R.: Quantifying point-prediction uncertainty in neural networks via residual estimation with an I/O Kernel. In: ICLR 2020, Addis Ababa, Ethiopia, pp. 1–17 (2019) Yau, J.W., et al.: Global prevalence and major risk factors of diabetic retinopathy. Diabetes Care 35(3), 556–564 (2012) Zeng, X., Chen, H., Luo, Y., Ye, W.: Automated diabetic retinopathy detection based on binocular Siamese-like convolutional neural network. IEEE Access 7(c), 30744–30753 (2019) |
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Gonzalez Osorio, Fabioafbb77c7b853278c83659a12e1b8dbe6600Perdomo Charry, Oscar Julianc280ba13fd48e8dbf9cdbc8179aa9c94600Toledo Cortes, Santiago599b76396f9dc557e94e41ec3b2f3331600De La Pava, Melissa176fe4dc82980a29aba79efc4179328a600GiBiome2021-05-12T19:00:05Z2021-10-01T17:16:54Z2021-05-122021-10-01T17:16:54Z20200302-9743https://repositorio.escuelaing.edu.co/handle/001/142510.1007/978-3-030-63419-3_21DOI:10.1007/978-3-030-63419-3_21La retinopatía diabética (RD) es una de las complicaciones microvasculares de la diabetes mellitus, que sigue siendo una de las principales causas de ceguera en todo el mundo. Los modelos computacionales basados en redes neuronales convolucionales representan el estado del arte para la detección automática de RD utilizando imágenes de fondo de ojo. La mayor parte del trabajo actual aborda este problema como una tarea de clasificación binaria. Sin embargo, incluir la estimación de leyes y la cuantificación de la incertidumbre de las predicciones puede aumentar potencialmente la solidez del modelo. En este artículo, se presenta un método de proceso híbrido de aprendizaje profundo y gaussiano para el diagnóstico de RD y la cuantificación de la incertidumbre. Este método combina el poder de representación del aprendizaje profundo con la capacidad de generalizar a partir de pequeños conjuntos de datos de modelos de procesos gaussianos. Los resultados muestran que la cuantificación de la incertidumbre en las predicciones mejora la interpretabilidad del método como herramienta de apoyo al diagnósticoDiabetic retinopathy (DR) is one of the microvascular complications of diabetes mellitus, which remains a leading cause of blindness worldwide. Computational models based on convolutional neural networks represent the state of the art for automatic detection of DR using fundus images. Most of the current work addresses this problem as a binary classification task. However, including law estimation and quantification of prediction uncertainty can potentially increase model robustness. In this paper, a hybrid deep learning and 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 data sets of Gaussian process models. The results show that the quantification of uncertainty in the predictions improves the interpretability of the method as a diagnostic support tool. Translated with www.DeepL.com/Translator (free version)Este trabajo fue parcialmente financiado por un premio de investigación de Google y por el proyecto Colciencias número 1101-807-63563.10 páginasapplication/pdfengSpringer Sciencehttps://link.springer.com/chapter/10.1007/978-3-030-63419-3_21Hybrid Deep Learning Gaussian Process for Diabetic Retinopathy Diagnosis and Uncertainty QuantificationArtículo de revistainfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARThttp://purl.org/coar/version/c_970fb48d4fbd8a85Lecture Notes in Computer Science (LNCS, volumen 12069)20621512069N/ALecture Notes in Computer ScienceAbrámoff, M.D., et al.: Automated analysis of retinal images for detection of referable diabetic retinopathy. JAMA Ophthalmol. 131(3), 351–357 (2013).American Academy of Ophthalmology: International clinical diabetic retinopathy disease severity scale detailed table. International Council of Ophthalmology (2002)Bradshaw, J., Matthews, A.G.d.G., Ghahramani, Z.: Adversarial examples, uncertainty, and transfer testing robustness in Gaussian process hybrid deep networks, eprint, pp. 1-33 (2017).Decenciére, E., et al.: Feedback on a publicly distributed image database: the Messidor database. Image Anal. Stereol. 33(3), 231–234 (2014).Diabetic Retinopathy Detection of Kaggle: Eyepacs challenge.Ethem, A.: Introduction to Machine Learning, 3rd edn. The MIT Press, Cambridge (2014)Gargeya, R., Leng, T.: Automated identification of diabetic retinopathy using deep learning. Ophthalmology 124(7), 962–969 (2017)Gulshan, V., et al.: Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA J. Am. Med. Assoc. 316(22), 2402–2410 (2016)Kaya, M., Bilge, H.: Deep metric learning: a survey. Symmetry 11, 1066 (2019).Krause, J., et al.: Grader variability and the importance of reference standards for evaluating machine learning models for diabetic retinopathy. Ophthalmology 125(8), 1264–1272 (2018).Leibig, C., Allken, V., Ayhan, M.S., Berens, P., Wahl, S.: Leveraging uncertainty information from deep neural networks for disease detection. Sci. Rep. 7(1), 1–14 (2017)Li, F., Liu, Z., Chen, H., Jiang, M., Zhang, X., Wu, Z.: Automatic detection of diabetic retinopathy in retinal fundus photographs based on deep learning algorithm. Transl. Vis. Sci. Technol. 8(6) (2019).Lim, Z.W., Lee, M.L., Hsu, W., Wong, T.Y.: Building trust in deep learning system towards automated disease detection. In: The Thirty-First AAAI Conference on Innovative Applications of Artificial Intelligence, pp. 9516–9521 (2018)Perdomo, O., Gonzalez, F.: A systematic review of deep learning methods applied to ocular images. Ciencia e Ingenieria Neogranadina 30(1), 9–26 (2019)Raghu, M., Blumer, K., Sayres, R., Obermeyer, Z., Kleinberg, R., Mullainathan, S., Kleinberg, J.: Direct uncertainty prediction for medical second opinions. In: Proceedings of the 36th International Conference on Machine Learning, PMLR 97, Long Beach, California (2019)Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, December 2016, pp. 2818–2826 (2016).Voets, M., Møllersen, K., Bongo, L.A.: Reproduction study using public data of: Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. PLoS ONE 14(6), 1–11 (2019)Wells, J.A., et al.: aflibercept, bevacizumab, or ranibizumab for diabetic macular edema two-year results from a comparative effectiveness randomized clinical trial. Ophthalmology 123(6), 1351–1359 (2016)Wilkinson, C.P.P., et al.: Proposed international clinical diabetic retinopathy and diabetic macular edema disease severity scales. Ophthalmology 110(9), 1677–1682 (2003)Wilson, A., Nickisch, H.: Kernel interpolation for scalable structured gaussian processes (KISS-GP). In: Proceedings of the 32nd International Conference on Machine Learning. JMLR: W&CP, Lille, France (2015)Xin, Q., Elliot, M., Miikkulainen, R.: Quantifying point-prediction uncertainty in neural networks via residual estimation with an I/O Kernel. In: ICLR 2020, Addis Ababa, Ethiopia, pp. 1–17 (2019)Yau, J.W., et al.: Global prevalence and major risk factors of diabetic retinopathy. Diabetes Care 35(3), 556–564 (2012)Zeng, X., Chen, H., Luo, Y., Ye, W.: Automated diabetic retinopathy detection based on binocular Siamese-like convolutional neural network. IEEE Access 7(c), 30744–30753 (2019)info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Retinopatía diabéticaAprendizajeMétodo gaussianoGaussian methodDeep LearningDiabetic RetinopathyGaussian ProcessUncertainty QuantificationAprendizaje profundoRetinopatía diabéticaProceso gaussianoCuantificación de la incertidumbreTEXTHybrid Deep Learning Gaussian Process for.pdf.txtHybrid Deep Learning Gaussian Process for.pdf.txtExtracted texttext/plain26327https://repositorio.escuelaing.edu.co/bitstream/001/1425/3/Hybrid%20Deep%20Learning%20Gaussian%20Process%20for.pdf.txta03e60b923aeb9258cab0d1a7df79b16MD53open accessTHUMBNAILHybrid Deep Learning Gaussian Process for.pdf.jpgHybrid Deep Learning Gaussian Process for.pdf.jpgGenerated Thumbnailimage/jpeg10506https://repositorio.escuelaing.edu.co/bitstream/001/1425/4/Hybrid%20Deep%20Learning%20Gaussian%20Process%20for.pdf.jpg6def9325bf09d7abcea6e49bb12b43f0MD54open accessLICENSElicense.txttext/plain1881https://repositorio.escuelaing.edu.co/bitstream/001/1425/1/license.txt5a7ca94c2e5326ee169f979d71d0f06eMD51open accessORIGINALHybrid Deep Learning Gaussian Process for.pdfapplication/pdf232426https://repositorio.escuelaing.edu.co/bitstream/001/1425/2/Hybrid%20Deep%20Learning%20Gaussian%20Process%20for.pdf94a9c5ad984ac88fd8c0e1906ccf5e48MD52open access001/1425oai:repositorio.escuelaing.edu.co:001/14252022-10-06 18:34:40.808open accessRepositorio Escuela Colombiana de Ingeniería Julio Garavitorepositorio.eci@escuelaing.edu.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 |