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

Author:

Toledo-Cortés SantiagoORCID,de la Pava MelissaORCID,Perdomo OscarORCID,González Fabio A.ORCID

Publisher

Springer International Publishing

Reference23 articles.

1. Abrámoff, M.D., et al.: Automated analysis of retinal images for detection of referable diabetic retinopathy. JAMA Ophthalmol. 131(3), 351–357 (2013). https://doi.org/10.1001/jamaophthalmol.2013.1743

2. American Academy of Ophthalmology: International clinical diabetic retinopathy disease severity scale detailed table. International Council of Ophthalmology (2002)

3. 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). arXiv:1707.02476v1

4. Decenciére, E., et al.: Feedback on a publicly distributed image database: the Messidor database. Image Anal. Stereol. 33(3), 231–234 (2014). https://doi.org/10.5566/ias.1155

5. Diabetic Retinopathy Detection of Kaggle: Eyepacs challenge. www.kaggle.com/c/diabetic-retinopathy-detection/data. Accessed 15 Oct 2019

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