Skip to main content
SpringerLink
Log in

Software Tools for Machine and Deep Learning

  • Chapter
  • First Online:
Machine and Deep Learning in Oncology, Medical Physics and Radiology

Abstract

With latest advancements in computational power and parallel processing technologies, the world of machine learning has recently made big strides and is gradually shifting towards one of its computationally expensive yet more accurate subset: Deep Learning. Consequently, many software tools and libraries have been developed recently for implementation of machine and deep learning algorithms. This chapter presents an overview of some of the most widely used popular contemporary machine and deep learning libraries.

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 99.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 129.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 179.99
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

References

  1. TIOBE Index for April 2020, Tiobe. (2020) https://www.tiobe.com/tiobe-index/ cited 2 April 2020.

  2. Helmus J, Understanding Conda and Pip, Anaconda. (2018), https://www.anaconda.Com/understanding-Conda-and-pip/. Cited 25 March 2020.

  3. Oliphant TE. A guide to NumPy. USA: Trelgol publishing; 2006.

    Google Scholar 

  4. van der Walt S, Colbert SC, Varoquaux G. The NumPy Array: a structure for efficient numerical computation. Comput Sci Eng. 2011;13:22–30.

    Article  Google Scholar 

  5. Virtanen P, Gommers R, Oliphant TE, Haberland M, Reddy T, Cournapeau D, Burovski E, Peterson P, Weckesser W, Bright J, van der Walt SJ, Brett M, Wilson J, Millman KJ, Mayorov N, Nelson ARJ, Jones E, Kern R, Larson E, Carey C, Polat I, Feng Y, Moore EW, VanderPlas J, Laxalde D, Perktold J, Cimrman R, Henriksen I, Quintero EA, Harris CR, Archibald AM, Ribeiro AH, Pedregosa F, van Mulbregt P. SciPy 1.0 Contributors, SciPy 1.0: fundamental algorithms for scientific computing in Python. Nat Methods. 2020;17:261âĂŞ272. https://doi.org/10.1038/s41592-019-0686-2.

    Article  Google Scholar 

  6. Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, Vanderplas J, Passos A, Cournapeau D, Brucher M, Perrot M, Duchesnay E. Scikit-learn: machine learning in python. J Mach Learn Res. 2011;12:2825–30.

    MathSciNet  MATH  Google Scholar 

  7. Buitinck L, Louppe G, Blondel M, Pedregosa F, Mueller A, Grisel O, Niculae V, Prettenhofer P, Gramfort A, Grobler J, Layton R, Vanderplas J, Joly A, Holt B, Varo-quaux G, API design for machine learning software: experiences from the scikit-learn project. 2013, arXiv:1309.0238[cs.LG].

    Google Scholar 

  8. scikit-learn developers, scikit-learn user guide, Release 0.22.2. 2020. https://scikit-learn.org/stable/downloads/scikit-learn-docs.pdf

  9. Sonnenburg S, Strathmann H, Lisitsyn S, Gal V, Iglesias GarcÃŋa FJ, Lin W, De S, Zhang C, Frx, Tklein23, Andreev E, Behr J, Sploving, Mazumdar P, Widmer C, Deng P, De Toni G, Mahindre S, Kislay A, Hughes K, Votyakov R, Khalednasr, Sharma S, Novik A, Panda A, Anagnostopoulos E, Pang L, Binder A, Serialhex, Björn Esser. Shogun-toolbox/shogun: Shogun 6.1.0. Zenodo; 2017. https://doi.org/10.5281/zenodo.1067840.

  10. Albanese D, Kessler FB, Visintainer R, Merler S, Riccadonna S, Jurman G, Furlanello C, mlpy: Machine Learning Python. 2012, arXiv:1202.6548 [cs.MS].

    Google Scholar 

  11. Hanke M, Halchenko YO, Sederberg PB, Hanson SJ, Haxby JV, Pollmann S. PyMVPA: a Python Toolbox for Multivariate Pattern Analysis of fMRI Data. Neuroinformatics. 2009;7:37âĂŞ53. https://doi.org/10.1007/s12021-008-9041-ya.

    Article  Google Scholar 

  12. Zito T, Wilbert N, Wiskott L, Berkes P. Modular toolkit for Data Process- ing (MDP): a Python data processing frame work. Front Neuroinform. 2009;2:8. https://doi.org/10.3389/neuro.11.008.2008.

    Article  Google Scholar 

  13. Schaul T, Bayer J, Wierstra D, Sun Y, Felder M, Sehnke F, Ruckstieb T, Schmidhuber J. PyBrain. J Mach Learn Res. 2010;11:743–6.

    Google Scholar 

  14. Al-Rfou R, Alain G, Almahairi A, Angermueller C, Bahdanau D, Ballas N, Bastien F, Bayer J, Belikov A, Belopolsky A, Bengio Y, Bergeron A, Bergstra J, Bisson V, Snyder JB, Bouchard N, Lewandowski NB, Bouthillier X, de Brebisson A, Breuleux O, Carrier P-L, Cho K, Chorowski J, Christiano P, Cooijmans T, Cote M-A, Cote M, Courville A, Dauphin YN, Delalleau O, Demouth J, Desjardins G, Dieleman S, Dinh L, Ducoffe M, Dumoulin V, Kahou SE, Erhan D, Fan Z, Firat O, Germain M, Glorot X, Goodfellow I, Graham M, Gulcehre C, Hamel P, Harlouchet I, Heng J-P, Hidasi B, Honari S, Jain A, Jean S, Jia K, Korobov M, Kulkarni V, Lamb A, Lamblin P, Larsen E, Laurent C, Lee S, Lefrancois S, Lemieux S, Leonard N, Lin Z, Livezey JA, Lorenz C, Lowin J, Ma Q, Manzagol P-A, Mastropietro O, McGibbon RT, Memisevic R, van Merrienboer B, Michalski V, Mirza M, Orlandi A, Pal C, Pascanu R, Pezeshki M, Raffel C, Renshaw D, Rocklin M, Romero A, Roth M, Sadowski P, Salvatier J, Savard F, Schluter J, Schulman J, Schwartz G, Serban IV, Serdyuk D, Shabanian S, Simon E, Spieckermann S, Subramanyam SR, Sygnowski J, Tanguay J, van Tulder G, Turian J, Urban S, Vincent P, Visin F, de Vries H, Farley DW, Webb DJ, Willson M, Xu K, Xue L, Yao L, Zhang S, Zhang Y, Theano: A Python framework for fast computation of mathematical expressions. 2016, arXiv:1605.02688 [cs.SC].

    Google Scholar 

  15. Tokui S, Okuta R, Akiba T, Niitani Y, Ogawa T, Saito S, Suzuki S, Uenishi K, Vogel B, Vincent HY, Chainer: A deep learning framework for accelerating the research cycle. 2019, arXiv:1908.00213 [cs.LG].

    Google Scholar 

  16. M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, S. Ghemawat, I. Goodfellow, A. Harp, G. Irving, M. Isard, R. Jozefowicz, Y. Jia, L. Kaiser, M. Kudlur, J. Levenberg, D. ManÃľ, M. Schuster, R. Monga, S. Moore, D. Murray, C. Olah, J. Shlens, B. Steiner, I. Sutskever, K. Talwar, P. Tucker, V. Vanhoucke, V. Vasudevan, F. ViÃľgas, O. Vinyals, P. Warden, M. Wattenberg, M. Wicke, Y. Yu, and X. Zheng, TensorFlow: Large-scale machine learning on heterogeneous systems. 2015. https://www.usenix.org/system/files/conference/osdi16/osdi16-abadi.pdf

  17. Paszke A, Gross S, Massa F, Lerer A, Bradbury J, Chanan G, Killeen T, Lin Z, Gimelshein N, Antiga L, Desmaison A, Kopf A, Yang E, De Vito Z, Raison M, Tejani A, Chilamkurthy S, Steiner B, Fang L, Bai J, Chin-Tala S. PyTorch: An Imperative Style, High-Performance Deep Learning Library, Advances in Neural Information Processing Systems, vol. 32. 2nd ed. New York: Curran Asso-ciates, Inc.; 2019. p. 8024–35. http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf

    Google Scholar 

  18. Jia Y, Shelhamer E, Donahue J, Karayev S, Long J, Girshick R, Guadarrama S, Darrell T. Caffe: Convolutional Architecture for Fast Feature Embedding. 2014, arXiv:1408.5093 [cs.CV].

    Google Scholar 

  19. Chen T, Li M, Li Y, Lin M, Wang N, Wang M, Xiao T, Xu B, Zhang C, Zhang Z. MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems. 2015, arXiv:1512.01274 [cs.DC].

    Google Scholar 

  20. Guyon I, Gunn S, Ben Hur A, Dror G. Result analysis of the NIPS 2003 feature selection challenge. In: NIPS’04: Proceedings of the 17th International Conference on Neural Information Processing Systems December 2004; 2004. p. 545âĂŞ552.

    Google Scholar 

  21. Deng J, Dong W, Socher R, Li L, Li K, Fei-Fei L. ImageNet: A large-scale hierarchical image database, 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, 2009, pp. 248–255.

    Google Scholar 

  22. Krizhevsky A. One weird trick for parallelizing convolutional neural networks. 2014, arXiv:1404.5997 [cs.NE]

    Google Scholar 

  23. P. Sermanet, D. Eigen, X. Zhang, M. Mathieu, R. Fergus, and Y. LeCun, OverFeat:Integrated Recognistion, Localization, and Detection using Convolution Networks. 2014, arXiv:1312.6229 [cs.CV].

    Google Scholar 

  24. Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. 2014, arXiv:1409.1556 [cs.CV].

    Google Scholar 

  25. Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, A. Rabinovich, Going Deeper with Convolutions. 2014, arXiv:1409.4842 [cs.CV].

    Google Scholar 

  26. Marcus M, Santorini B, Marcinkiewicz MA. Building a Large Annotated Corpus of English: The Penn Treebank. 1993. https://repository.upenn.edu/cis_reports/237/.

  27. Frank E, Hall MA, Witten IH. The WEKA Workbench. Online Appendix for“Data Mining: Practical Machine Learning Tools and Techniques”, Morgan Kaufmann, 4th edn. 2016. https://www.cs.waikato.ac.nz/ml/weka/Witten\textunderscoreet\textunderscoreal\textunderscore2016\textunderscoreappendix.pdf.

Download references

Author information

Authors and Affiliations

  1. H. Lee Moffitt Cancer and Research Institute, Tampa, FL, USA

    Dipesh Niraula & Issam El Naqa

Authors
  1. Dipesh Niraula
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Issam El Naqa
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Dipesh Niraula .

Editor information

Editors and Affiliations

  1. Department of Machine Learning, Moffitt Cancer Center, Tampa, FL, USA

    Issam El Naqa

  2. Department of Radiation Oncology, Virginia Commonwealth University, Richmond, VA, USA

    Martin J. Murphy

Rights and permissions

Reprints and permissions

Copyright information

© 2022 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Niraula, D., El Naqa, I. (2022). Software Tools for Machine and Deep Learning. In: El Naqa, I., Murphy, M.J. (eds) Machine and Deep Learning in Oncology, Medical Physics and Radiology. Springer, Cham. https://doi.org/10.1007/978-3-030-83047-2_7

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-83047-2_7

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-83046-5

  • Online ISBN: 978-3-030-83047-2

  • eBook Packages: MedicineMedicine (R0)

Publish with us

Policies and ethics

Search

Navigation