Abstract
Training deep convolutional neural network for classification in medical tasks is often difficult due to the lack of annotated data samples. Deep convolutional networks (CNN) has been successfully used as an automatic detection tool to support the grading of diabetic retinopathy and macular edema. Nevertheless, the manual annotation of exudates in eye fundus images used to classify the grade of the DR is very time consuming and repetitive for clinical personnel. Active learning algorithms seek to reduce the labeling effort in training machine learning models. This work presents a label-efficient CNN model using the expected gradient length, an active learning algorithm to select the most informative patches and images, converging earlier and to a better local optimum than the usual SGD (Stochastic Gradient Descent) strategy. Our method also generates useful masks for prediction and segments regions of interest.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Decencière, E., Cazuguel, G., Zhang, X., Thibault, G., Klein, J.C., Meyer, F., Marcotegui, B., Quellec, G., Lamard, M., Danno, R., et al.: Teleophta: machine learning and image processing methods for teleophthalmology. IRBM 34(2), 196–203 (2013)
Freytag, A., Rodner, E., Denzler, J.: Selecting influential examples: active learning with expected model output changes. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8692, pp. 562–577. Springer, Cham (2014). doi:10.1007/978-3-319-10593-2_37
Greenspan, H., van Ginneken, B., Summers, R.M.: Guest editorial deep learning in medical imaging: overview and future promise of an exciting new technique. IEEE Trans. Med. Imaging 35(5), 1153–1159 (2016)
Gulshan, V., Peng, L., Coram, M., Stumpe, M.C., Wu, D., Narayanaswamy, A., Venugopalan, S., Widner, K., Madams, T., Cuadros, J., et al.: Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA 316(22), 2402–2410 (2016)
Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., Darrell, T.: Caffe: convolutional architecture for fast feature embedding. In: Proceedings of the 22nd ACM International Conference on Multimedia, pp. 675–678. ACM (2014)
Kauppi, T., et al.: Eye fundus image analysis for automatic detection of diabetic retinopathy. Lappeenranta University of Technology (2010)
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)
Perdomo, O., Otalora, S., Rodríguez, F., Arevalo, J., González, F.A.: A novel machine learning model based on exudate localization to detect diabetic macular edema. In: Ophthalmic Medical Image Analysis Third International Workshop (OMIA 2016), pp. 137–144. University of Iowa (2016)
Sánchez, C.I., Niemeijer, M., Abràmoff, M.D., van Ginneken, B.: Active learning for an efficient training strategy of computer-aided diagnosis systems: application to diabetic retinopathy screening. In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (eds.) MICCAI 2010. LNCS, vol. 6363, pp. 603–610. Springer, Heidelberg (2010). doi:10.1007/978-3-642-15711-0_75
Settles, B.: Active learning literature survey. Univ. Wis. Madison 52(55–66), 11 (2010)
Settles, B., Craven, M., Ray, S.: Multiple-instance active learning. In: Advances in Neural Information Processing Systems, pp. 1289–1296 (2008)
Stitt, A.W., Lois, N., Medina, R.J., Adamson, P., Curtis, T.M.: Advances in our understanding of diabetic retinopathy. Clin. Sci. 125(1), 1–17 (2013)
Yu, F., Seff, A., Zhang, Y., Song, S., Funkhouser, T., Xiao, J.: LSUN: construction of a large-scale image dataset using deep learning with humans in the loop. arXiv preprint arXiv:1506.03365 (2015)
Zhou, S., Chen, Q., Wang, X.: Active semi-supervised learning method with hybrid deep belief networks. PLoS ONE 9(9), e107122 (2014)
Acknowledgments
This work was supported by the Administrative Department of Science, Technology and Innovation of Colombia (Colciencias) through the grant Jóvenes Investigadores 2014 in call 645 and by Nvidia with a TitanX GPU.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Otálora, S., Perdomo, O., González, F., Müller, H. (2017). Training Deep Convolutional Neural Networks with Active Learning for Exudate Classification in Eye Fundus Images. In: Cardoso, M., et al. Intravascular Imaging and Computer Assisted Stenting, and Large-Scale Annotation of Biomedical Data and Expert Label Synthesis. LABELS STENT CVII 2017 2017 2017. Lecture Notes in Computer Science(), vol 10552. Springer, Cham. https://doi.org/10.1007/978-3-319-67534-3_16
Download citation
DOI: https://doi.org/10.1007/978-3-319-67534-3_16
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-67533-6
Online ISBN: 978-3-319-67534-3
eBook Packages: Computer ScienceComputer Science (R0)