Skip to main content
SpringerLink
Log in

Innovative Deep Learning Approach for Biomedical Data Instantiation and Visualization

  • Chapter
  • First Online:
Deep Learning for Biomedical Data Analysis

Abstract

One of the main problems that most of biomedical applications face, is represented by the massive amount of unlabeled data. Manually analyzing and classifying massive database by human expert is mostly unfeasible, being—in certain limited conditions (still, extremely time-consuming)—partially been done, only for simple signatures, easily recognizable by an expert. Concerning this aspect, medical experts face two challenging problems: how to select the most significant data for labeling, and what is the minimum size of the data set—but sufficient to define each pathology—to perform the training of the classifier. In this chapter, we propose a new method, based on a visual data analysis, to build an efficient classifier with a minimum of labeled data. An encoder, part of a Convolutional Variational Autoencoder (CVAE), is used as a data projection for a 2D-visualization. The input vectors are encoded into a 2D-latent space, which helps the expert to visually analyze the spatial distribution of the training data set.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    https://keras.io/.

References

  1. Alvarado-Díaz W, Lima P, Meneses-Claudio B, Roman-Gonzalez A (2017) Implementation of a brain-machine interface for controlling a wheelchair. In: 2017 CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies (CHILECON), pp 1–6, DOI 10.1109/CHILECON.2017.8229668

    Google Scholar 

  2. Angermueller C, Pärnamaa T, Parts L, Stegle O (2016) Deep learning for computational biology. Molecular Systems Biology 12(7), DOI 10.15252/msb.20156651, URL https://msb.embopress.org/content/12/7/878, https://msb.embopress.org/content/12/7/878.full.pdf

  3. Baltres A, Zeina AM, et al RZ (2020) Prediction of oncotype dx recurrence score using deep multi layer perceptrons in estrogen receptor-positive, her2 negative breast cancer. Breast Cancer 27(5):1007–1016, DOI https://doi.org/10.1007/s12282-020-01100-4

    Google Scholar 

  4. Bengio Y (2014) How auto-encoders could provide credit assignment in deep networks via target propagation. CoRR URL https://arxiv.org/abs/1407.7906

  5. Bengio Y, Lamblin P, Popovici D, Larochelle H (2007) Greedy layer-wise training of deep networks. In: Schölkopf B, Platt J, Hoffman T (eds) Advances in Neural Information Processing Systems (NIPS 06), MIT Press, pp 153–160, DOI https://www.iro.umontreal.ca/~lisa/pointeurs/BengioNips2006All.pdf

  6. Blei DM, Kucukelbir A, McAuliffe JD (2016) Variational inference: A review for statisticians. arXiv e-prints arXiv:1601.00670, https://arxiv.org/abs/1601.00670

  7. Cao C, Liu F, Tan H, Song D, Shu W, Li W, Zhou Y, Bo X, Xie Z (2018) Deep learning and its applications in biomedicine. Genomics, Proteomics & Bioinformatics 16(1):17–32, DOI https://doi.org/10.1016/j.gpb.2017.07.003, URL https://www.sciencedirect.com/science/article/pii/S1672022918300020

  8. Chandra B, Sharma RK (2016) Deep learning with adaptive learning rate using laplacian score. Expert Systems with Applications 63:1–7, DOI https://doi.org/10.1016/j.eswa.2016.05.022

  9. Creswell A, White T, Dumoulin V, Arulkumaran K, Sengupta B, Bharath AA (2018) Generative adversarial networks: An overview. IEEE Signal Processing Magazine 35(1):53–65, DOI 10.1109/MSP.2017.2765202

    Google Scholar 

  10. Dhamala J, Ghimire S, Sapp JL, Horáček BM, Wang L (2018) High-dimensional bayesian optimization of personalized cardiac model parameters via an embedded generative model. In: Frangi AF, Schnabel JA, Davatzikos C, Alberola-López C, Fichtinger G (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2018, Springer International Publishing, Cham, pp 499–507

    Chapter  Google Scholar 

  11. Ditzler G, Roveri M, Alippi C, Polikar R (2015) Learning in nonstationary environments: A survey. IEEE Computational Intelligence Magazine 10(4):12–25, DOI 10.1109/MCI.2015.2471196

    Google Scholar 

  12. Dua D, Graff C (2017) UCI machine learning repository. URL https://archive.ics.uci.edu/ml

  13. Fan YJ (2019) Autoencoder node saliency: Selecting relevant latent representations. Pattern Recognition 88:643–653, DOI https://doi.org/10.1016/j.patcog.2018.12.015, URL https://www.sciencedirect.com/science/article/pii/S0031320318304369

  14. Fnaiech N, Fnaiech F, Jervis BW (2011) Feedforward NeuralNetworks Pruning Algorithms, Industrial Electronics Handbook, vol 5, j.d. irwin, 2nd edn, chap 15, pp 15–1 to 15–15

    Google Scholar 

  15. Ghimire S, Dhamala J, Gyawali PK, Sapp JL, Horacek M, Wang L (2018) Generative modeling and inverse imaging of cardiac transmembrane potential. In: Frangi AF, Schnabel JA, Davatzikos C, Alberola-López C, Fichtinger G (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2018, Springer International Publishing, Cham, pp 508–516

    Chapter  Google Scholar 

  16. Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2014) Generative adversarial nets. In: Ghahramani Z, Welling M, Cortes C, Lawrence ND, Weinberger KQ (eds) Advances in Neural Information Processing Systems 27, Curran Associates, Inc., pp 2672–2680, URL https://papers.nips.cc/paper/5423-generative-adversarial-nets.pdf

  17. Goodfellow IJ, Warde-farley D, Mirza M, Courville A, Bengio Y (2013) Maxout networks. In: In ICML

    Google Scholar 

  18. Han HG, Qiao JF (2013) A structure optimisation algorithm for feedforward neural network construction. Neurocomputing 99:347–357, DOI https://dx.doi.org/10.1016/j.neucom.2012.07.023

  19. Han L, Yin Z (2018) A cascaded refinement gan for phase contrast microscopy image super resolution. In: Frangi AF, Schnabel JA, Davatzikos C, Alberola-López C, Fichtinger G (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2018, Springer International Publishing, Cham, pp 347–355

    Chapter  Google Scholar 

  20. He H, Garcia EA (2009) Learning from imbalanced data. IEEE Transactions on Knowledge and Data Engineering 21(9):1263–1284, DOI 10.1109/TKDE.2008.239

    Google Scholar 

  21. Hinton GE, Osindero S, Teh YW (2006) A fast learning algorithm for deep belief nets. Neural Comput 18(7):1527–1554, DOI 10.1162/neco.2006.18.7.1527

    Google Scholar 

  22. Jones W, Alasoo K, Fishman D, Parts L (2017) Computational biology: deep learning. Emerging Topics in Life Sciences 1(3):257–274, DOI 10.1042/ETLS20160025, URL https://www.emergtoplifesci.org/content/1/3/257, https://www.emergtoplifesci.org/content/1/3/257.full.pdf

  23. Kingma D (2017) Variational inference & deep learning: A new synthesis. PhD thesis, Faculty of Science (FNWI), Informatics Institute (IVI), University of Amsterdam, URL https://hdl.handle.net/11245.1/8e55e07f-e4be-458f-a929-2f9bc2d169e8

  24. Kingma DP, Welling M (2013) Auto-Encoding Variational Bayes. arXiv e-prints arXiv:1312.6114, https://arxiv.org/abs/1312.6114

  25. Lee S, Kwak M, Tsui KL, Kim SB (2019) Process monitoring using variational autoencoder for high-dimensional nonlinear processes. Engineering Applications of Artificial Intelligence 83:13–27, DOI ’https://doi.org/10.1016/j.engappai.2019.04.013, URL https://www.sciencedirect.com/science/article/pii/S0952197619300983

  26. Li Z, Nguyen SP, Xu D, Shang Y (2017) Protein loop modeling using deep generative adversarial network. In: 2017 IEEE 29th International Conference on Tools with Artificial Intelligence (ICTAI), pp 1085–1091, DOI 10.1109/ICTAI.2017.00166

    Google Scholar 

  27. Litjens G, Kooi T, Bejnordi BE, Setio AAA, Ciompi F, Ghafoorian M, van der Laak JA, van Ginneken B, Sánchez CI (2017) A survey on deep learning in medical image analysis. Medical Image Analysis 42:60–88, DOI https://doi.org/10.1016/j.media.2017.07.005, URL https://www.sciencedirect.com/science/article/pii/S1361841517301135

  28. van der Maaten L, Hinton G (2008) Visualizing data using t-sne. Journal of Machine Learning Research 9(11):2579–2605

    Google Scholar 

  29. Mahmud M, Kaiser M, Hussain A, Vassanelli S (2018) Applications of deep learning and reinforcement learning to biological data. IEEE Trans Neural Netw Learn Syst DOI 10.1109/TNNLS.2018.2790388.

    Google Scholar 

  30. Min S, Lee B, Yoon S (2016) Deep learning in bioinformatics. CoRR abs/1603.06430, URL https://arxiv.org/abs/1603.06430

  31. Montavon G, Lapuschkin S, Binder A, Samek W, Müller KR (2017) Explaining nonlinear classification decisions with deep taylor decomposition. Pattern Recognition 65:211–222, DOI https://doi.org/10.1016/j.patcog.2016.11.008, URL https://www.sciencedirect.com/science/article/pii/S0031320316303582

  32. Nakamura K, Hong B (2019) Adaptive weight decay for deep neural networks. CoRR abs/1907.08931, URL https://arxiv.org/abs/1907.08931

  33. Pérez-Sánchez B, Fontenla-Romero O, Guijarro-Berdiñas B (2016) A review of adaptive online learning for artificial neural networks. Artificial Intelligence Review DOI 10.1007/s10462-016-9526-2

    Google Scholar 

  34. Raví D, Wong C, Deligianni F, Berthelot M, Andreu-Perez J, Lo B, Yang GZ (2017) Deep learning for health informatics. IEEE Journal of Biomedical and Health Informatics 21(1):4–21, DOI 10.1109/JBHI.2016.2636665

    Google Scholar 

  35. Ren J, Hacihaliloglu I, Singer EA, Foran DJ, Qi X (2018) Adversarial domain adaptation for classification of prostate histopathology whole-slide images. In: Frangi AF, Schnabel JA, Davatzikos C, Alberola-López C, Fichtinger G (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2018, Springer International Publishing, Cham, pp 201–209

    Chapter  Google Scholar 

  36. Rumelhart DE, Hinton GE, Williams RJ (1986) Learning representations by back-propagating errors. Nature 323:533 EP –, URL https://dx.doi.org/10.1038/323533a0

  37. Schmidhuber J (2015) Deep learning in neural networks: An overview. Neural Networks 61:85–117, DOI https://doi.org/10.1016/j.neunet.2014.09.003

  38. Selvaraju RR, Das A, Vedantam R, Cogswell M, Parikh D, Batra D (2016) Grad-cam: Why did you say that? visual explanations from deep networks via gradient-based localization. CoRR abs/1610.02391, URL https://arxiv.org/abs/1610.02391

  39. Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R (2014) Dropout: A simple way to prevent neural networks from overfitting. Journal of Machine Learning Research 15:1929–1958, DOI https://jmlr.org/papers/v15/srivastava14a.html

  40. Street WN, Wolberg WH, Mangasarian OL (1993) Nuclear feature extraction for breast tumor diagnosis. International Symposium on Electronic Imaging Science and Technology 1905, DOI 10.1117/12.148698, URL https://doi.org/10.1117/12.148698

  41. Tan JH, Hagiwara Y, Pang W, Lim I, Oh SL, Adam M, Tan RS, Chen M, Acharya UR (2018) Application of stacked convolutional and long short-term memory network for accurate identification of cad ecg signals. Computers in Biology and Medicine 94:19–26, DOI https://doi.org/10.1016/j.compbiomed.2017.12.023, URL https://www.sciencedirect.com/science/article/pii/S0010482517304201

  42. Yu H, Yang X, Zheng S, Sun C (2018) Active learning from imbalanced data: A solution of online weighted extreme learning machine. IEEE Transactions on Neural Networks and Learning Systems pp 1–16, DOI 10.1109/TNNLS.2018.2855446

    Google Scholar 

  43. Yu S, Príncipe JC (2019) Understanding autoencoders with information theoretic concepts. Neural Networks 117:104–123, DOI https://doi.org/10.1016/j.neunet.2019.05.003, URL https://www.sciencedirect.com/science/article/pii/S0893608019301352

  44. Zeiler MD, Fergus R (2014) Visualizing and understanding convolutional networks. In: Fleet D, Pajdla T, Schiele B, Tuytelaars T (eds) Computer Vision – ECCV 2014, Springer International Publishing, Cham, pp 818–833

    Chapter  Google Scholar 

  45. Zemouri R (2020) Semi-supervised adversarial variational autoencoder. Machine Learning and Knowledge Extraction (MAKE) 2(3):361–378, DOI https://doi.org/10.3390/make2030020

  46. Zemouri R, Devalland C, Valmary-Degano S, Zerhouni N (2019) Intelligence artificielle : quel avenir en anatomie pathologique ? Annales de Pathologie 39(2):119–129, DOI https://doi.org/10.1016/j.annpat.2019.01.004, URL https://www.sciencedirect.com/science/article/pii/S0242649819300203, l’anatomopathologie augmentée

  47. Zemouri R, Omri N, Fnaiech F, Zerhouni N, Fnaiech N (2019) A new growing pruning deep learning neural network algorithm (gp-dlnn). Neural Computing and Applications DOI 10.1007/s00521-019-04196-8, URL https://doi.org/10.1007/s00521-019-04196-8

  48. Zemouri R, Zerhouni N, Racoceanu D (2019) Deep learning in the biomedical applications: Recent and future status. Applied Sciences 9(8), DOI 10.3390/app9081526, URL https://www.mdpi.com/2076-3417/9/8/1526

  49. Zemouri R, Lévesque M, Amyot N, Hudon C, Kokoko O, Tahan SA (2020) Deep convolutional variational autoencoder as a 2d-visualization tool for partial discharge source classification in hydrogenerators. IEEE Access 8:5438–5454, DOI 10.1109/ACCESS.2019.2962775

    Google Scholar 

  50. Zhang Z, Jiang T, Zhan C, Yang Y (2019) Gaussian feature learning based on variational autoencoder for improving nonlinear process monitoring. Journal of Process Control 75:136–155, DOI https://doi.org/10.1016/j.jprocont.2019.01.008, URL https://www.sciencedirect.com/science/article/pii/S095915241930037X

Download references

Author information

Authors and Affiliations

  1. CEDRIC Laboratory of the Conservatoire National des Arts et Metiers (CNAM), HESAM University, Paris, France

    Ryad Zemouri

  2. Sorbonne University and Paris Brain Institute, Paris, France

    Daniel Racoceanu

Authors
  1. Ryad Zemouri
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Daniel Racoceanu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ryad Zemouri .

Editor information

Editors and Affiliations

  1. Computing and Information Technology, The University of Bisha, Bisha, Saudi Arabia

    Mourad Elloumi

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Zemouri, R., Racoceanu, D. (2021). Innovative Deep Learning Approach for Biomedical Data Instantiation and Visualization. In: Elloumi, M. (eds) Deep Learning for Biomedical Data Analysis. Springer, Cham. https://doi.org/10.1007/978-3-030-71676-9_8

Download citation

Publish with us

Policies and ethics

Search

Navigation