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.
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References
Mitchell TM. Machine learning. New York: McGraw-Hill; 1997.
Alpaydin E. Introduction to machine learning. 3rd ed. Cambridge, MA: The MIT Press; 2014.
Bishop CM. Pattern recognition and machine learning. New York: Springer; 2006.
Apolloni B. Machine learning and robot perception. Berlin: Springer; 2005.
Ao S-I, Rieger BB, Amouzegar MA. Machine learning and systems engineering. Dordrecht/New York: Springer; 2010.
Györfi L, Ottucsák G, Walk H. Machine learning for financial engineering. Singapore/London: World Scientific; 2012.
Gong Y, Xu W. Machine learning for multimedia content analysis. New York/London: Springer; 2007.
Yu J, Tao D. Modern machine learning techniques and their applications in cartoon animation research. 1st ed. Hoboken: Wiley; 2013.
Fielding A. Machine learning methods for ecological applications. Boston: Kluwer Academic Publishers; 1999.
Mitra S. Introduction to machine learning and bioinformatics. Boca Raton: CRC Press; 2008.
Yang ZR. Machine learning approaches to bioinformatics. Hackensack: World Scientific; 2010.
Cleophas TJ. Machine learning in medicine. New York: Springer; 2013.
Malley JD, Malley KG, Pajevic S. Statistical learning for biomedical data. Cambridge: Cambridge University Press; 2011.
Ifrah G. The universal history of computing: from the abacus to the quantum computer. New York: John Wiley; 2001.
Samuel AL. Some studies in machine learning using the game of checkers. IBM: J Res Dev. 1959;3:210–29.
Rosenblatt F. The perceptron: a probabilistic model for information storage and organization in the brain. Psychol Rev. 1958;65:386–408.
Minsky ML, Papert S. Perceptrons; an introduction to computational geometry. Cambridge, MA: MIT Press; 1969.
Werbos PJ. Beyond regression: new tools for prediction and analysis in the behavioral sciences; PhD thesis, Harvard University, 1974.
Quinlan JR. Induction of decision trees. Mach Learn. 1986;1:81–106.
Cortes C, Vapnik V. Support-vector networks. Mach Learn. 1995;20:273–97.
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.
Breiman L. Random forests. Mach Learn. 2001;45:5–32.
Hinton GE. Learning multiple layers of representation. Trends Cogn Sci. 2007;11:428–34.
Bengio Y, Courville A, Vincent P. Representation learning: a review and new perspectives. IEEE Trans Pattern Anal Mach Intell. 2013;35:1798–828.
Cherkassky VS, Mulier F. Learning from data: concepts, theory, and methods. 2nd ed. Hoboken: IEEE Press/Wiley-Interscience; 2007.
Kargupta H. Next generation of data mining. Boca Raton: CRC Press; 2009.
Vapnik VN. Statistical learning theory. New York: Wiley; 1998.
Mitchell TM. The need for biases in learning generalizations. New Brunswick: Rutgers University; 1980.
Sutton RS, Barto AG. Reinforcement learning: an introduction. Cambridge, MA: MIT Press; 1998.
Hebb DO. The organization of behavior; a neuropsychological theory. New York: Wiley; 1949.
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.
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.
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.
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.
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.
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.
Tweedie R, Mengersen K, Eccleston J. Garbage in, garbage out: can statisticians quantify the effects of poor data? Chance. 1994;7:20–7.
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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
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DOI: https://doi.org/10.1007/978-3-319-18305-3_1
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