Abstract
Domain adaptation is an attractive approach given the availability of a large amount of labeled data with similar properties but different domains. It is effective in image classification tasks where obtaining sufficient label data is challenging. We propose a novel method, named SELDA, for stacking ensemble learning via extending three domain adaptation methods for effectively solving real-world problems. The major assumption is that when base domain adaptation models are combined, we can obtain a more accurate and robust model by exploiting the ability of each of the base models. We extend Maximum Mean Discrepancy (MMD), Low-rank coding, and Correlation Alignment (CORAL) to compute the adaptation loss in three base models. Also, we utilize a two-fully connected layer network as a meta-model to stack the output predictions of these three well-performing domain adaptation models to obtain high accuracy in ophthalmic image classification tasks. The experimental results using Age-Related Eye Disease Study (AREDS) benchmark ophthalmic dataset demonstrate the effectiveness of the proposed model.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Burlina, P.M., Joshi, N., Pacheco, K.D., Freund, D.E., Kong, J., Bressler, N.M.: Use of deep learning for detailed severity characterization and estimation of 5-year risk among patients with age-related macular degeneration. JAMA ophthalmol. 136(12), 1359–1366 (2018)
Burlina, P.M., Joshi, N., Pekala, M., Pacheco, K.D., Freund, D.E., Bressler, N.M.: Automated grading of age-related macular degeneration from color fundus images using deep convolutional neural networks. JAMA Ophthalmol. 135(11), 1170–1176 (2017)
Chen, C., Chen, Z., Jiang, B., Jin, X.: Joint domain alignment and discriminative feature learning for unsupervised deep domain adaptation. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 3296–3303 (2019)
Cheng, Z., Chen, C., Chen, Z., Fang, K., Jin, X.: Robust and high-order correlation alignment for unsupervised domain adaptation. Neural Comput. Appl. 1–13 (2021)
Davis, M.D., et al.: The age-related eye disease study severity scale for age-related macular degeneration: AREDS report no. 17. Arch. Ophthalmol. (Chicago, Ill.: 1960) 123(11), 1484–1498 (2005)
Deng, W., Zheng, L., Sun, Y., Jiao, J.: Rethinking triplet loss for domain adaptation. IEEE Trans. Circuits Syst. Video Technol. 31, 29–37 (2020)
Ding, Z., Fu, Y.: Deep transfer low-rank coding for cross-domain learning. IEEE Trans. Neural Netw. Learn. Syst. 30(6), 1768–1779 (2018)
Grassmann, F., et al.: A deep learning algorithm for prediction of age-related eye disease study severity scale for age-related macular degeneration from color fundus photography. Ophthalmology 125(9), 1410–1420 (2018)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Kang, G., Jiang, L., Yang, Y., Hauptmann, A.G.: Contrastive adaptation network for unsupervised domain adaptation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4893–4902 (2019)
Li, J., Li, Z., Lü, S.: Feature concatenation for adversarial domain adaptation. Expert Sys. Appl. 169, 114490 (2021)
Lin, Z., Chen, M., Ma, Y.: The augmented lagrange multiplier method for exact recovery of corrupted low-rank matrices. arXiv preprint arXiv:1009.5055 (2010)
Liu, G., Lin, Z., Yan, S., Sun, J., Yu, Y., Ma, Y.: Robust recovery of subspace structures by low-rank representation. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 171–184 (2012)
Long, M., Zhu, H., Wang, J., Jordan, M.I.: Deep transfer learning with joint adaptation networks. In: International Conference on Machine Learning, pp. 2208–2217 (2017)
Madadi, Y., Seydi, V., Hosseini, R.: Deep unsupervised domain adaptation for image classification via low rank representation learning. J. Adv. Comput. Res. 11(1), 57–67 (2020)
Madadi, Y., Seydi, V., Nasrollahi, K., Hosseini, R., Moeslund, T.B.: Deep visual unsupervised domain adaptation for classification tasks: a survey. IET Image Process. (2020). https://doi.org/10.1049/iet-ipr.2020.0087
Pavlyshenko, B.: Using stacking approaches for machine learning models. In: 2018 IEEE Second International Conference on Data Stream Mining & Processing (DSMP), pp. 255–258. IEEE (2018)
Peng, Y., et al.: DeepSeeNet: a deep learning model for automated classification of patient-based age-related macular degeneration severity from color fundus photographs. Ophthalmology 126(4), 565–575 (2019)
Rahman, M.M., Fookes, C., Baktashmotlagh, M., Sridharan, S.: On minimum discrepancy estimation for deep domain adaptation. In: Singh, R., Vatsa, M., Patel, V.M., Ratha, N. (eds.) Domain Adaptation for Visual Understanding, pp. 81–94. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-30671-7_6
Study, T.A.R.E.D., et al.: The age-related eye disease study (AREDS): design implications AREDS report no. 1. Controlled Clin. Trials 20(6), 573–600 (1999)
Zhu, Y., Zhuang, F., Wang, D.: Aligning domain-specific distribution and classifier for cross-domain classification from multiple sources. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, 5989–5996 (2019)
Zhu, Y., et al.: Multi-representation adaptation network for cross-domain image classification. Neural Netw. 119, 214–221 (2019)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Madadi, Y., Seydi, V., Sun, J., Chaum, E., Yousefi, S. (2021). Stacking Ensemble Learning in Deep Domain Adaptation for Ophthalmic Image Classification. In: Fu, H., Garvin, M.K., MacGillivray, T., Xu, Y., Zheng, Y. (eds) Ophthalmic Medical Image Analysis. OMIA 2021. Lecture Notes in Computer Science(), vol 12970. Springer, Cham. https://doi.org/10.1007/978-3-030-87000-3_18
Download citation
DOI: https://doi.org/10.1007/978-3-030-87000-3_18
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-86999-1
Online ISBN: 978-3-030-87000-3
eBook Packages: Computer ScienceComputer Science (R0)