Transformed representations for convolutional neural networks in diabetic retinopathy screening

AAAI Workshop - Technical Report

Volumn WS-14-08, Issue , 2014, Pages 21-25

  Lim, Gilbert ^a   Lee, Mong Li ^a   Hsu, Wynne ^a   Wong, Tien Yin ^b  

^a NATIONAL UNIVERSITY OF SINGAPORE   (Singapore)

^b SINGAPORE EYE RESEARCH INSTITUTE   (Singapore)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; CONVOLUTION; EYE PROTECTION; NEURAL NETWORKS;

BIOLOGICALLY INSPIRED; CONVOLUTIONAL NEURAL NETWORK; DIABETIC RETINOPATHY SCREENING; GENERAL KNOWLEDGE; MULTI-LAYER PERCEPTION; PRE-SEGMENTATION; RETINAL IMAGE; SPATIAL TRANSFORMATION;

IMAGE SEGMENTATION;

EID: 84974791417     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (14)

1. Ciresan, D.; Meier, U.; Masci, J.; Gambardella, L. M.; and Schmidhuber, J. 2011. Flexible, high performance convolu- Tional neural networks for image classification. In Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence (IJCAI), 1237-1242. AAAI Press.
2. Ciresan, D.; Giusti, A.; Schmidhuber, J.; et al. 2012. Deep neural networks segment neuronal membranes in electron microscopy images. In Advances in Neural Information Processing Systems 25, 2852-2860.
3. Ciresan, D.; Giusti, A.; Gambardella, L. M.; and Schmidhuber, J. 2013. Mitosis detection in breast cancer histology images with deep neural networks. In MICCAI, 411-418.
4. Deng, J.; Dong, W.; Socher, R.; Li, L.-J.; Li, K.; and Fei- Fei, L. 2009. Imagenet: A large-scale hierarchical image database. In Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on, 248-255. IEEE.
5. Hubel, D. H and Wiesel, T. N. 1968. Receptive fields and functional architecture of monkey striate cortex. The Journal of physiology 195(1):215-243.
6. Kauppi, T.; Kalesnykiene, V.; Kamarainen, J.-K.; Lensu, L.; Sorri, I.; Raninen, A.; Voutilainen, R.; Uusitalo, H.; Kalviainen, H.; and Pietila, J. 2007. The diaretdbl diabetic retinopathy database and evaluation protocol. In BMVC, 1-10.
7. Krizhevsky, A., and Hinton, G. 2009. Learning multiple layers of features from tiny images. Master's thesis, Department of Computer Science, University of Toronto.
8. Krizhevsky, A.; Sutskever, I.; and Hinton, G. 2012. Imagenet classification with deep convolutional neural networks. In Advances in Neural Information Processing Systems 25, 1106-1114.
9. LeCun, Y.; Bottou, L.; Bengio, Y.; and Haffner, P. 1998. Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11):2278-2324.
10. Lim, G.; Lee, M. L.; and Hsu, W. 2012. Constrained- mser detection of retinal pathology. In Pattern Recognition (ICPR), 2012 21st International Conference on, 2059-2062. IEEE.
11. Mikolajczyk, K., and Schmid, C. 2004. Comparison of affine-invariant local detectors and descriptors. In Proc. European Signal Processing Conf.
12. Scherer, D.; Miiller, A.; and Behnke, S. 2010. Evaluation of pooling operations in convolutional architectures for object recognition. In Artificial Neural Networks-ICANN 2010. Springer. 92-101.
13. Schulz, H., and Behnke, S. 2012. Learning object-class segmentation with convolutional neural networks. In 11th European Symposium on Artificial Neural Networks (ESANN), Volume 3.
14. Szegedy, C.; Toshev, A.; and Erhan, D. 2013. Deep neural networks for object detection. In Advances in Neural Information Processing Systems 26, 2553-2561.