Skip to main content
SpringerLink
Log in

An On-Going Framework for Easily Experimenting with Deep Learning Models for Bioimaging Analysis

  • Conference paper
  • First Online:
Book cover Distributed Computing and Artificial Intelligence, Special Sessions, 15th International Conference (DCAI 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 801))

  • 688 Accesses

Abstract

Due to the broad use of deep learning methods in Bioimaging, it seems convenient to create a framework that facilitates the task of analysing different models and selecting the best one to solve each particular problem. In this work-in-progress, we are developing a Python framework to deal with such a task in the case of bioimage classification. Namely, the purpose of the framework is to automate and facilitate the process of choosing the best combination of feature extractors (obtained from transfer learning and other techniques), and classification models. The features and models to test are fixed by a simple configuration file to facilitate the use of the framework by non-expert users. The best model is automatically selected through a statistical study, and then it can be employed to predict the category of new images.

This work was partially supported by Ministerio de Economía, Industria y Competitividad [MTM2014-54151-P, MTM2017-88804-P], and Agencia de Desarrollo Económico de La Rioja [ADER-2017-I-IDD-00018].

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

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Ronneberger, O., Fischer, P., Brox, T.: U-Net: Convolutional Networks for Biomedical Image Segmentation (2015). http://arxiv.org/abs/1505.04597

    Google Scholar 

  2. Akkus, Z., Galimzianova, A., Hoogi, A., Rubin, D.L., Erickson, B.J.: Deep learning for brain MRI segmentation: state of the art and future directions. J. Digit. Imaging 30(4), 449–459 (2017)

    Article  Google Scholar 

  3. Nauman, M., Ur Rehman, H., Politano, G., Benso, A.: Beyond Homology Transfer: Deep Learning for Automated Annotation of Proteins. bioRxiv (2017). https://www.biorxiv.org/content/early/2017/07/25/168120

  4. Reif, M., Shafait, F., Goldstein, M., Breuel, T., Dengel, A.: Automatic classifier selection for non-experts. Pattern Anal. Appl. 17(1), 83–96 (2012)

    Article  MathSciNet  Google Scholar 

  5. Ware, M., Frank, E., Holmes, G., Hall, M., Witten, I.H.: Interactive machine learning: letting users build classifiers. Int. J. Hum. Comput. Stud. 55(3), 281–292 (2001)

    Article  Google Scholar 

  6. Google: Google cloud automl (2018). https://cloud.google.com/automl/

  7. Sheskin, D.: Handbook of Parametric and Nonparametric Statistical Procedures. CRC Press, Boca Raton (2011)

    MATH  Google Scholar 

  8. Intel: Opencv (2016). https://opencv.org/

  9. Chollet, F., et al.: Keras (2017). https://keras.io/

Download references

Author information

Authors and Affiliations

  1. Dpto. de Matemáticas y Computación, Universidad de La Rioja, Edificio CCT, Madre de Dios, 53, 26006, Logroño, La Rioja, Spain

    Manuel García, César Domínguez, Jónathan Heras, Eloy Mata & Vico Pascual

Authors
  1. Manuel García
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. César Domínguez
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jónathan Heras
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Eloy Mata
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Vico Pascual
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Manuel García .

Editor information

Editors and Affiliations

  1. BISITE Digital Innovation Hub, University of Salamanca, Salamanca, Spain

    Sara Rodríguez

  2. BISITE Digital Innovation Hub, University of Salamanca, Salamanca, Spain

    Javier Prieto

  3. GECAD - Instituto Superior de Engenharia, Porto, Portugal

    Pedro Faria

  4. Department of Computer Science and Production Management, University of Zielona Góra, Zielona Góra, Poland

    Sławomir Kłos

  5. Computing Science and Artificial Intelligence, Rey Juan Carlos University, Móstoles, Madrid, Spain

    Alberto Fernández

  6. Basque Center for Applied Mathematics, Bilbao, Spain

    Santiago Mazuelas

  7. Basque Center for Applied Mathematics, Universidad de Alcalá, Alcalá de Henares, Spain

    M. Dolores Jiménez-López

  8. Departamento de Informática y Automática, University of Salamanca, Salamanca, Spain

    María N. Moreno

  9. Departamento de Sistemas Informáticos, University of Castilla-La Mancha, Albacete, Spain

    Elena M. Navarro

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

García, M., Domínguez, C., Heras, J., Mata, E., Pascual, V. (2019). An On-Going Framework for Easily Experimenting with Deep Learning Models for Bioimaging Analysis. In: Rodríguez, S., et al. Distributed Computing and Artificial Intelligence, Special Sessions, 15th International Conference. DCAI 2018. Advances in Intelligent Systems and Computing, vol 801. Springer, Cham. https://doi.org/10.1007/978-3-319-99608-0_39

Download citation

Publish with us

Policies and ethics

Search

Navigation