Top

Published in:

2019 | OriginalPaper | Chapter

Active Image Data Augmentation

Authors : Flávio Arthur Oliveira Santos, Cleber Zanchettin, Leonardo Nogueira Matos, Paulo Novais

Published in: Hybrid Artificial Intelligent Systems

Publisher: Springer International Publishing

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

Deep neural networks models have achieved state-of-the-art results in a great number of different tasks in different domains (e.g., natural language processing and computer vision). However, the notions of robustness, causality, and fairness are not measured in traditional evaluated settings. In this work, we proposed an active data augmentation method to improve the model robustness to new data. We use the Vanilla Backpropagation to visualize what the trained model consider important in the input information. Based on that information, we augment the training dataset with new data to refine the model training. The objective is to make the model robust and effective for important input information. We evaluated our approach in a Spinal Cord Gray Matter Segmentation task and verified improvement in robustness while keeping the model competitive in the traditional metrics. Besides, we achieve the state-of-the-art results on that task using a U-Net based model.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

previous chapter Towards a Custom Designed Mechanism for Indexing and Retrieving Video Transcripts

next chapter A Novel Density-Based Clustering Approach for Outlier Detection in High-Dimensional Data

http://niftyweb.cs.ucl.ac.uk/program.php?p=CHALLENGE.

Antol, S., et al.: VQA: visual question answering. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2425–2433 (2015)

Bishop, C.M.: Pattern Recognition and Machine Learning. Springer, New York (2006)MATH

Dice, L.R.: Measures of the amount of ecologic association between species. Ecology 26(3), 297–302 (1945)CrossRef

Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2672–2680 (2014)

He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. CoRR abs/1512.03385 (2015). http://arxiv.org/abs/1512.03385

Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)

LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436 (2015)CrossRef

LeCun, Y., Bengio, Y., et al.: Convolutional networks for images, speech, and time series. In: The Handbook of Brain Theory and Neural Networks, vol. 3361, no. 10 (1995)

Mikołajczyk, A., Grochowski, M.: Data augmentation for improving deep learning in image classification problem. In: 2018 International Interdisciplinary PhD Workshop (IIPhDW), pp. 117–122. IEEE (2018)

10.

Pereira, S., Meier, R., Alves, V., Reyes, M., Silva, C.A.: Automatic brain tumor grading from MRI data using convolutional neural networks and quality assessment. In: Stoyanov, D., et al. (eds.) MLCN/DLF/IMIMIC -2018. LNCS, vol. 11038, pp. 106–114. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-02628-8_12CrossRef

11.

Pereira, S., Pinto, A., Alves, V., Silva, C.A.: Brain tumor segmentation using convolutional neural networks in MRI images. IEEE Trans. Med. Imaging 35(5), 1240–1251 (2016)CrossRef

12.

Perez, L., Wang, J.: The effectiveness of data augmentation in image classification using deep learning. arXiv preprint arXiv:1712.04621 (2017)

13.

Perone, C.S., Calabrese, E., Cohen-Adad, J.: Spinal cord gray matter segmentation using deep dilated convolutions. Sci. Rep. 8(1), 5966 (2018)CrossRef

14.

Porisky, A., et al.: Grey matter segmentation in spinal cord MRIs via 3D convolutional encoder networks with shortcut connections. In: Cardoso, M.J., et al. (eds.) DLMIA/ML-CDS -2017. LNCS, vol. 10553, pp. 330–337. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-67558-9_38CrossRef

15.

Prados, F.: Spinal cord grey matter segmentation challenge. Neuroimage 152, 312–329 (2017)CrossRef

16.

Rieke, J., Eitel, F., Weygandt, M., Haynes, J., Ritter, K.: Visualizing convolutional networks for MRI-based diagnosis of Alzheimer’s disease. CoRR abs/1808.02874 (2018). http://arxiv.org/abs/1808.02874

17.

Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28CrossRef

18.

Simonyan, K., Vedaldi, A., Zisserman, A.: Deep inside convolutional networks: visualising image classification models and saliency maps. arXiv preprint arXiv:1312.6034 (2013)

19.

Springenberg, J.T., Dosovitskiy, A., Brox, T., Riedmiller, M.: Striving for simplicity: the all convolutional net. arXiv preprint arXiv:1412.6806 (2014)

20.

Xie, X., Li, Y., Shen, L.: Active learning for breast cancer identification. CoRR abs/1804.06670 (2018). http://arxiv.org/abs/1804.06670

21.

Zeiler, M.D., Fergus, R.: Visualizing and understanding convolutional networks. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8689, pp. 818–833. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10590-1_53CrossRef

22.

Zeiler, M.D., Krishnan, D., Taylor, G.W., Fergus, R.: Deconvolutional networks (2010)

23.

Zhang, Q., Cao, R., Shi, F., Wu, Y.N., Zhu, S.C.: Interpreting CNN knowledge via an explanatory graph. In: Thirty-Second AAAI Conference on Artificial Intelligence (2018)

24.

Zhang, Y., Li, K., Li, K., Wang, L., Zhong, B., Fu, Y.: Image super-resolution using very deep residual channel attention networks. CoRR abs/1807.02758 (2018). http://arxiv.org/abs/1807.02758

Title: Active Image Data Augmentation
Authors: Flávio Arthur Oliveira Santos
Cleber Zanchettin
Leonardo Nogueira Matos
Paulo Novais
Publisher: Springer International Publishing
Book: Hybrid Artificial Intelligent Systems
Print ISBN: 978-3-030-29858-6

Electronic ISBN: 978-3-030-29859-3

Copyright Year: 2019
DOI: https://doi.org/10.1007/978-3-030-29859-3_27

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Premium Partner