Skip to main content
Springer Nature Link
Log in

What Is Machine Learning?

  • Chapter
Machine Learning in Radiation Oncology

Abstract

Machine learning is an evolving branch of computational algorithms that are designed to emulate human intelligence by learning from the surrounding environment. They are considered the working horse in the new era of the so-called big data. Techniques based on machine learning have been applied successfully in diverse fields ranging from pattern recognition, computer vision, spacecraft engineering, finance, entertainment, and computational biology to biomedical and medical applications. More than half of the patients with cancer receive ionizing radiation (radiotherapy) as part of their treatment, and it is the main treatment modality at advanced stages of local disease. Radiotherapy involves a large set of processes that not only span the period from consultation to treatment but also extend beyond that to ensure that the patients have received the prescribed radiation dose and are responding well. The degrees of the complexity of these processes can vary and may involve several stages of sophisticated human-machine interactions and decision making, which would naturally invite the use of machine learning algorithms into optimizing and automating these processes including but not limited to radiation physics quality assurance, contouring and treatment planning, image-guided radiotherapy, respiratory motion management, treatment response modeling, and outcomes prediction. The ability of machine learning algorithms to learn from current context and generalize into unseen tasks would allow improvements in both the safety and efficacy of radiotherapy practice leading to better outcomes.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
€34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
EUR 29.95
Price includes VAT (Germany)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
EUR 71.68
Price includes VAT (Germany)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
EUR 90.94
Price includes VAT (Germany)
  • 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

') var buybox = document.querySelector("[data-id=id_"+ timestamp +"]").parentNode var buyingOptions = buybox.querySelectorAll(".buying-option") ;[].slice.call(buyingOptions).forEach(initCollapsibles) var buyboxMaxSingleColumnWidth = 480 function initCollapsibles(subscription, index) { var toggle = subscription.querySelector(".buying-option-price") subscription.classList.remove("expanded") var form = subscription.querySelector(".buying-option-form") var priceInfo = subscription.querySelector(".price-info") var buyingOption = toggle.parentElement if (toggle && form && priceInfo) { toggle.setAttribute("role", "button") toggle.setAttribute("tabindex", "0") toggle.addEventListener("click", function (event) { var expandedBuyingOptions = buybox.querySelectorAll(".buying-option.expanded") var buyboxWidth = buybox.offsetWidth ;[].slice.call(expandedBuyingOptions).forEach(function(option) { if (buyboxWidth buyboxMaxSingleColumnWidth) { toggle.click() } else { if (index === 0) { toggle.click() } else { toggle.setAttribute("aria-expanded", "false") form.hidden = "hidden" priceInfo.hidden = "hidden" } } }) } initialStateOpen() if (window.buyboxInitialised) return window.buyboxInitialised = true initKeyControls() })()

Institutional subscriptions

Similar content being viewed by others

References

  1. Mitchell TM. Machine learning. New York: McGraw-Hill; 1997.

    Google Scholar 

  2. Alpaydin E. Introduction to machine learning. 3rd ed. Cambridge, MA: The MIT Press; 2014.

    Google Scholar 

  3. Bishop CM. Pattern recognition and machine learning. New York: Springer; 2006.

    Google Scholar 

  4. Apolloni B. Machine learning and robot perception. Berlin: Springer; 2005.

    Book  Google Scholar 

  5. Ao S-I, Rieger BB, Amouzegar MA. Machine learning and systems engineering. Dordrecht/New York: Springer; 2010.

    Book  Google Scholar 

  6. Györfi L, Ottucsák G, Walk H. Machine learning for financial engineering. Singapore/London: World Scientific; 2012.

    Google Scholar 

  7. Gong Y, Xu W. Machine learning for multimedia content analysis. New York/London: Springer; 2007.

    Google Scholar 

  8. Yu J, Tao D. Modern machine learning techniques and their applications in cartoon animation research. 1st ed. Hoboken: Wiley; 2013.

    Book  Google Scholar 

  9. Fielding A. Machine learning methods for ecological applications. Boston: Kluwer Academic Publishers; 1999.

    Book  Google Scholar 

  10. Mitra S. Introduction to machine learning and bioinformatics. Boca Raton: CRC Press; 2008.

    Google Scholar 

  11. Yang ZR. Machine learning approaches to bioinformatics. Hackensack: World Scientific; 2010.

    Book  Google Scholar 

  12. Cleophas TJ. Machine learning in medicine. New York: Springer; 2013.

    Book  Google Scholar 

  13. Malley JD, Malley KG, Pajevic S. Statistical learning for biomedical data. Cambridge: Cambridge University Press; 2011.

    Book  Google Scholar 

  14. Ifrah G. The universal history of computing: from the abacus to the quantum computer. New York: John Wiley; 2001.

    Google Scholar 

  15. Samuel AL. Some studies in machine learning using the game of checkers. IBM: J Res Dev. 1959;3:210–29.

    Article  Google Scholar 

  16. Rosenblatt F. The perceptron: a probabilistic model for information storage and organization in the brain. Psychol Rev. 1958;65:386–408.

    Article  CAS  PubMed  Google Scholar 

  17. Minsky ML, Papert S. Perceptrons; an introduction to computational geometry. Cambridge, MA: MIT Press; 1969.

    Google Scholar 

  18. Werbos PJ. Beyond regression: new tools for prediction and analysis in the behavioral sciences; PhD thesis, Harvard University, 1974.

    Google Scholar 

  19. Quinlan JR. Induction of decision trees. Mach Learn. 1986;1:81–106.

    Google Scholar 

  20. Cortes C, Vapnik V. Support-vector networks. Mach Learn. 1995;20:273–97.

    Google Scholar 

  21. Schapire RE. A brief introduction to boosting. In: Proceedings of the 16th international joint conference on artificial intelligence, vol. 2. Stockholm: Morgan Kaufmann Publishers Inc; 1999. p. 1401–6.

    Google Scholar 

  22. Breiman L. Random forests. Mach Learn. 2001;45:5–32.

    Article  Google Scholar 

  23. Hinton GE. Learning multiple layers of representation. Trends Cogn Sci. 2007;11:428–34.

    Article  PubMed  Google Scholar 

  24. Bengio Y, Courville A, Vincent P. Representation learning: a review and new perspectives. IEEE Trans Pattern Anal Mach Intell. 2013;35:1798–828.

    Article  PubMed  Google Scholar 

  25. Cherkassky VS, Mulier F. Learning from data: concepts, theory, and methods. 2nd ed. Hoboken: IEEE Press/Wiley-Interscience; 2007.

    Book  Google Scholar 

  26. Kargupta H. Next generation of data mining. Boca Raton: CRC Press; 2009.

    Google Scholar 

  27. Vapnik VN. Statistical learning theory. New York: Wiley; 1998.

    Google Scholar 

  28. Mitchell TM. The need for biases in learning generalizations. New Brunswick: Rutgers University; 1980.

    Google Scholar 

  29. Sutton RS, Barto AG. Reinforcement learning: an introduction. Cambridge, MA: MIT Press; 1998.

    Google Scholar 

  30. Hebb DO. The organization of behavior; a neuropsychological theory. New York: Wiley; 1949.

    Google Scholar 

  31. El-Naqa I, Yang Y, Wernick MN, Galatsanos NP, Nishikawa RM. A support vector machine approach for detection of microcalcifications. IEEE Trans Med Imaging. 2002;21:1552–63.

    Article  PubMed  Google Scholar 

  32. Gurcan MN, Chan HP, Sahiner B, Hadjiiski L, Petrick N, Helvie MA. Optimal neural network architecture selection: improvement in computerized detection of microcalcifications. Acad Radiol. 2002;9:420–9.

    Article  PubMed  Google Scholar 

  33. El-Naqa I, Yang Y, Galatsanos NP, Nishikawa RM, Wernick MN. A similarity learning approach to content-based image retrieval: application to digital mammography. IEEE Trans Med Imaging. 2004;23:1233–44.

    Article  PubMed  Google Scholar 

  34. Gulliford SL, Webb S, Rowbottom CG, Corne DW, Dearnaley DP. Use of artificial neural networks to predict biological outcomes for patients receiving radical radiotherapy of the prostate. Radiother Oncol. 2004;71:3–12.

    Article  PubMed  Google Scholar 

  35. Munley MT, Lo JY, Sibley GS, Bentel GC, Anscher MS, Marks LB. A neural network to predict symptomatic lung injury. Phys Med Biol. 1999;44:2241–9.

    Article  CAS  PubMed  Google Scholar 

  36. Su M, Miften M, Whiddon C, Sun X, Light K, Marks L. An artificial neural network for predicting the incidence of radiation pneumonitis. Med Phys. 2005;32:318–25.

    Article  PubMed  Google Scholar 

  37. Tweedie R, Mengersen K, Eccleston J. Garbage in, garbage out: can statisticians quantify the effects of poor data? Chance. 1994;7:20–7.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Oncology, McGill University, Montreal, QC, Canada

    Issam El Naqa

  2. Department of Radiation Oncology, University of Michigan, Ann Arbor, USA

    Issam El Naqa

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

    Martin J. Murphy

Authors
  1. Issam El Naqa
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Martin J. Murphy
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Issam El Naqa .

Editor information

Editors and Affiliations

  1. Department of Oncology, McGill University, Montreal, Canada

    Issam El Naqa

  2. Department of Radiation Oncology, Stanford University School of Medicine, Stanford, California, USA

    Ruijiang Li

  3. Department of Radiation Oncology, Virginia Commonwealth University, Richmond, Virginia, USA

    Martin J. Murphy

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this chapter

Cite this chapter

El Naqa, I., Murphy, M.J. (2015). What Is Machine Learning?. In: El Naqa, I., Li, R., Murphy, M. (eds) Machine Learning in Radiation Oncology. Springer, Cham. https://doi.org/10.1007/978-3-319-18305-3_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-18305-3_1

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-18304-6

  • Online ISBN: 978-3-319-18305-3

  • eBook Packages: MedicineMedicine (R0)

Publish with us

Policies and ethics