Abstract
Detection of Diabetic Retinopathy (DR) has been worked on for a long time, but no commercially viable solutions that work for different populations exist yet. In this work, we investigate the performance of Very Deep Networks for the binary classification of fundus images provided by EyePACS as part of Kaggle’s DR detection challenge.
B. Akilesh, T. Marwah authors are equally contributed to this chapter as the first authors.
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Notes
- 1.
* denotes regularization with added gradient noise.
References
M. Abrà moff et al., Evaluation of a system for automatic detection of diabetic retinopathy from color fundus photographs in a large population of patients with diabetes. Diabetes Care 31(2), 193–198 (2008)
EyePACS, Identifying signs of diabetic retinopathy in eye images (2015), https://www.kaggle.com/c/diabetic-retinopathy-detection/data
G.G. Gardner et al., Automatic detection of diabetic retinopathy using an artificial neural network: a screening tool. Br. J. Ophthalmol. 80(11), 940–944 (1996)
K. He, X. Zhang, S. Ren, J. Sun, Deep residual learning for image recognition (2015), arXiv:1512.03385
A. Krizhevsky et al., Imagenet classification with deep convolutional neural networks, in NIPS (2012), pp. 1097–1105
A. Neelakantan et al., Adding gradient noise improves learning for very deep networks (2015), arXiv:1511.06807
Team o_O. Identifying signs of diabetic retinopathy in eye images (2016), https://github.com/sveitser/kaggle_diabetic. Accessed 20 Aug 2016
A. Osareh et al., Automated identification of diabetic retinal exudates in digital colour images. Br. J. Ophthalmol. 87(10), 1220–1223 (2003)
C.N. Straehle et al., Multiple instance learning with response-optimized random forests, in ICPR (2014), pp. 3768–3773
D. Usher et al., Automated detection of diabetic retinopathy in digital retinal images: a tool for diabetic retinopathy screening. Diabet. Med. 21(1), 84–90 (2004)
T. Walter et al., A contribution of image processing to the diagnosis of diabetic retinopathy-detection of exudates in color fundus images of the human retina. IEEE Trans. Med. Imaging 21(10), 1236–1243 (2002)
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Akilesh, B., Marwah, T., Balasubramanian, V.N., Rajamani, K. (2017). On the Relevance of Very Deep Networks for Diabetic Retinopathy Diagnostics. In: Contractor, D., Telang, A. (eds) Applications of Cognitive Computing Systems and IBM Watson . Springer, Singapore. https://doi.org/10.1007/978-981-10-6418-0_6
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DOI: https://doi.org/10.1007/978-981-10-6418-0_6
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