TY - CHAP
T1 - Hybrid Deep Learning Gaussian Process for Diabetic Retinopathy Diagnosis and Uncertainty Quantification
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.
PY - 2020/11/20
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
AB - 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
UR - http://www.scopus.com/inward/record.url?scp=85097434243&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85097434243&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-63419-3_21
DO - 10.1007/978-3-030-63419-3_21
M3 - Chapter (peer-reviewed)
AN - SCOPUS:85097434243
SN - 978-3-030-63418-6
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 206
EP - 215
BT - Ophthalmic Medical Image Analysis - 7th International Workshop, OMIA 2020, Held in Conjunction with MICCAI 2020, Proceedings
A2 - Fu, Huazhu
A2 - Garvin, Mona K.
A2 - MacGillivray, Tom
A2 - Xu, Yanwu
A2 - Zheng, Yalin
PB - Springer Science and Business Media Deutschland GmbH
T2 - 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
Y2 - 8 October 2020 through 8 October 2020
ER -
