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Innovation in Medicine and Healthcare Systems, and Multimedia pp 61–69Cite as

Data Augmentation for Building an Ensemble of Convolutional Neural Networks

Part of the Smart Innovation, Systems and Technologies book series (SIST,volume 145)

Abstract

Bioimage classification is important in subcellular localization, accurate cell phenotype recognition, and histopathological classification, to name a few applications. In this paper, we propose an ensemble of deep learning methods built using different batch sizes, different learning rates, and different methods of data augmentation. Our main goal is to test different methods of data augmentation for building an ensemble that boosts the performance of Convolutional Neural Networks (CNN). Our method is evaluated on a diverse set of bioimage classification problems, with each represented by a benchmark dataset and with each bioimage classification task representing a typical cellular or tissue-level classification problem. The results on these datasets demonstrate that the proposed ensemble does indeed boost the performance of the standard CNN. The MATLAB code of all the descriptors and experiments reported in this paper is available at https://github.com/LorisNanni.

Keywords

  • Microscopy imaging classification
  • Deep learning
  • Convolutional neural networks
  • Support vector machines

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References

  1. Schermelleh, L., Heintzmann, R., Leonhardt, H.: A guide to super-resolution fluorescence microscopy. J. Cell Biol. 190, 165–175 (2010)

    CrossRef  Google Scholar 

  2. Zhou, J., Lamichhane, S., Sterne, G., Ye, B., Peng, H.: BIOCAT: a pattern recognition platform for customizable biological image classification and annotation. BMC Bioinformatics 14, 291 (2013)

    CrossRef  Google Scholar 

  3. Misselwitz, B., Strittmatter, G., Periaswamy, B., Schlumberger, M.C., Rout, S., Horvath, P., Kozak, K., Hardt, W.D.: Enhanced cell classifier: a multi-class classification tool for microscopy images. BMC Bioinformat. 11 (2010)

    Google Scholar 

  4. Pau, G., Fuchs, F., Sklyar, O., Boutros, M., Huber, W.: EBImage—an R package for image processing with applications to cellular phenotypes. Bioinformatics 26 (2010)

    CrossRef  Google Scholar 

  5. Uhlmann, V., Singh, S., Carpenter, A.E.: CP-CHARM: segmentation-free image classification made accessible. BMC Bioinformatics 17, 51 (2016)

    CrossRef  Google Scholar 

  6. Rui, Y., Huang, T.S., Chang, S.F.: Image retrieval: current techniques, promising directions, and open issues. J. Vis. Commun. Image Represent. 10, 39–62 (1999)

    CrossRef  Google Scholar 

  7. Vailaya, A., Figueiredo, M.A.T., Jain, A.K., Zhang, H.J.: Image classification for content-based indexing. IEEE Trans. Image Process. 10, 117–130 (2001)

    CrossRef  Google Scholar 

  8. Huang, K., Murphy, R.F.: Automated classification of subcellular patterns in multicell images without segmentation into single cells. IEEE International Symposium on Biomedical Imaging: From Nano to Macro (ISBI’04), pp. 1139–11342. IEEE, Arlington (2004)

    Google Scholar 

  9. Glory, E., Murphy, R.: Automated subcellular location determination and high throughput microscopy. Dev. Cell 12, 7–16 (2007)

    CrossRef  Google Scholar 

  10. Yang, F., Xu, Y.Y., Shen, H.B.: Many local pattern texture features: which is better for image-based multilabel human protein subcellular localization classification. Scientif. World J. 2014, 429049 (2014)

    Google Scholar 

  11. Fogel, I., Sagi, D.: Gabor filters as texture discriminator. Biol. Cybern. 61, 103–113 (1989)

    CrossRef  Google Scholar 

  12. Haralick, R.M., Shanmugam, K., Dinstein, I.: Textural features for image classification. IEEE Trans. Systems Man Cybernet. 3, 610–621 (1973)

    CrossRef  Google Scholar 

  13. Nanni, L., Brahnam, S., Ghidoni, S., Menegatti, E., Barrier, T.: A comparison of methods for extracting information from the co-occurrence matrix for subcellular classification. Expert Syst. Appl. 40, 7457–7467 (2013)

    CrossRef  Google Scholar 

  14. Xu, Y., Zhu, J., Chang, E.I., Lai, M., Tu, Z.: Weakly supervised histopathology cancer image segmentation and classification. Med. Image Anal. 18, 591–604 (2014)

    CrossRef  Google Scholar 

  15. Barker, J., Hoogi, A., Depeursinge, A., Rubin, D.L.: Automated classification of brain tumor type in whole-slide digital pathology images using local representative tiles. Med. Image Anal. 30, 60–71 (2016)

    CrossRef  Google Scholar 

  16. Cristianini, N., Shawe-Taylor, J.: An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods. Cambridge University Press, Cambridge, UK (2000)

    CrossRef  Google Scholar 

  17. Schmidhuber, J.: Deep learning in neural networks: an overview. Neural Networks 61, 85–117 (2015)

    CrossRef  Google Scholar 

  18. LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)

    CrossRef  Google Scholar 

  19. Yosinski, J., Clune, J., Bengio, Y., Lipson, H.: How Transferable are Features in Deep Neural Networks? Cornell University (2014)

    Google Scholar 

  20. Araújo, T., Aresta, G., Castro, E., Rouco, J., Aguiar, P., Eloy, C., Polónia, A., Campilho, A.: Classification of breast cancer histology images using convolutional neural networks. PLoS ONE 12, 1–14 (2017)

    Google Scholar 

  21. Mateen, M., Wen, J., Nasrullah, Song, S., Huang, Z.: Fundus image classification using VGG-19 architecture with PCA and SVD. Symmetry 11 (2018)

    CrossRef  Google Scholar 

  22. Badejo, J.A., Adetiba, E., Akinrinmade, A., Akanle, M.B.: medical image classification with hand-designed or machine-designed texture descriptors: a performance evaluation. In: Bioinformatics and Biomedical Engineering, pp. 266–275. Springer International Publishing

    Google Scholar 

  23. Krizhevsky, A., Sutskever, I., Hinton, G.E.: COPY ImageNet classification with deep convolutional neural networks. In: Bartlett, P.L., Pereira, F.C.N., Burges, C.J.C., Bottou, L., Weinberger, K.Q. (eds.) Adv Neural Inf Process Syst, pp. 1106–1114. Curran Associates Inc, Lake Tahoe, NV (2012)

    Google Scholar 

  24. Shijie, J., Ping, W., Peiyi, J., Siping, H.: Research on data augmentation for image classification based on convolution neural networks. Chinese Automation Congress (CAC) 2017, pp. 4165–4170, Jinan (2017)

    Google Scholar 

  25. 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)

    Google Scholar 

  26. Frid-Adar, M., Diamant, I., Klang, E., Amitai, M., Goldberger, J., Greenspan, H.: GAN-based synthetic medical image augmentation for increased CNN performance in liver lesion classification. Neurocomputing 321, 321–331 (2018)

    CrossRef  Google Scholar 

  27. Zhao, D., Zhu, D., Lu, J., Luo, Y., Zhang, G.: Synthetic medical images using F&BGAN for improved lung nodules classification by multi-scale VGG16. Symmetry 10, 519 (2018)

    CrossRef  Google Scholar 

  28. Ding, J., Chen, B., Liu, H., Huang, M.: Convolutional neural network with data augmentation for sar target recognition. IEEE Geosci. Remote Sens. Lett. 13, 364–368 (2016)

    Google Scholar 

  29. Castro, E., Cardoso, J.S., Pereira, J.C.: Elastic Deformations for Data Augmentation in Breast Cancer Mass Detection, pp. 230–234 (2018)

    Google Scholar 

  30. Tajbakhsh, N., Shin, J.Y., Gurudu, S.R., Hurst, R.T., Kendall, C.B., Gotway, M.B., Liang, J.: Convolutional neural networks for medical image analysis: full training or fine tuning? IEEE Trans. Med. Imaging 35, 1299–1312 (2016)

    CrossRef  Google Scholar 

  31. Xu, Y., Jia, Z., Wang, L.-B., Ai, Y., Zhang, F., Lai, M., Chang, E.I.C.: Large scale tissue histopathology image classification, segmentation, and visualization via deep convolutional activation features. BMC Bioinformatics 18, 281 (2017)

    CrossRef  Google Scholar 

  32. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770–778. IEEE, Las Vegas, NV (2016)

    Google Scholar 

  33. Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely Connected Convolutional Networks. CVPR 1, 3 (2017)

    Google Scholar 

  34. Boland, M.V., Murphy, R.F.: A neural network classifier capable of recognizing the patterns of all major subcellular structures in fluorescence microscope images of HeLa cells. Bioinformatics 17, 1213–1223 (2001)

    CrossRef  Google Scholar 

  35. Shamir, L., Orlov, N.V., Eckley, D.M., Goldberg, I.: IICBU 2008: a proposed benchmark suite for biological image analysis. Med. Biol. Eng. Compu. 46, 943–947 (2008)

    CrossRef  Google Scholar 

  36. Tan, X., Triggs, B.: Enhanced local texture feature sets for face recognition under difficult lighting conditions. Analysis and Modelling of Faces and Gestures LNCS 4778, 168–182 (2007)

    MATH  Google Scholar 

  37. Guo, Z., Zhang, L., Zhang, D.: A completed modeling of local binary pattern operator for texture classification. IEEE Trans. Image Process. 19, 1657–1663 (2010)

    CrossRef  MathSciNet  Google Scholar 

  38. Nosaka, R., Fukui, K.: HEp-2 cell classification using rotation invariant co-occurrence among local binary patterns. Pattern Recognition in Bioinformatics 47, 2428–2436 (2014)

    CrossRef  Google Scholar 

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Acknowledgements

We gratefully acknowledge the support of NVIDIA Corporation with their donation of the Titan XP GPU used in this research.

Author information

Authors and Affiliations

  1. Department of Information Engineering, University of Padua, Via Gradenigo 6, 35131, Padua, Italy

    Loris Nanni & Gianluca Maguolo

  2. Computer Information Systems, Missouri State University, 901 S. National, 65804, Springfield, MO, USA

    Sheryl Brahnam

Authors
  1. Loris Nanni
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  2. Sheryl Brahnam
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  3. Gianluca Maguolo
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Corresponding author

Correspondence to Sheryl Brahnam .

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Editors and Affiliations

  1. Kusuatsu, Shiga, Japan

    Prof. Yen-Wei Chen

  2. Faculty of Informatics, Reutlingen University, Reutlingen, Baden-Württemberg, Germany

    Prof. Dr. Alfred Zimmermann

  3. Shoreham-by-sea, UK

    Prof. Robert J. Howlett

  4. University of Canberra, Canberra, ACT, Australia

    Prof. Dr. Lakhmi C. Jain

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Nanni, L., Brahnam, S., Maguolo, G. (2019). Data Augmentation for Building an Ensemble of Convolutional Neural Networks. In: Chen, YW., Zimmermann, A., Howlett, R., Jain, L. (eds) Innovation in Medicine and Healthcare Systems, and Multimedia. Smart Innovation, Systems and Technologies, vol 145. Springer, Singapore. https://doi.org/10.1007/978-981-13-8566-7_6

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