Front Matter

Ao, Sio-long; Gelman, Len; Kim, Haeng Kon; Amouzegar, Mahyar A.

doi:10.1142/9789813230774_fmatter

Cited by 1 publication

(2 citation statements)

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“…The term was coined by Arthur Samuel in his seminal work in the 1950s where he described machine learning as “a field of study that gives computers the ability to learn without being explicitly programmed.” Machine learning as a branch of the artificial intelligence field draws upon ideas from diverse disciplines such as probability and statistics, information theory, psychology, control theory, and philosophy . It has been successfully applied to many different fields including pattern recognition, computer vision, spacecraft engineering, finance, computational biology, and medical applications . Developed ML algorithms are currently considered one of the main workhorses in the new era of Big Data to potentially overcome challenges related to the excessive burden of manual curation, data veracity, and the analysis of complex patterns.…”

Section: Introductionmentioning

confidence: 99%

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Machine learning and modeling: Data, validation, communication challenges

et al. 2018

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With the era of big data, the utilization of machine learning algorithms in radiation oncology is rapidly growing with applications including: treatment response modeling, treatment planning, contouring, organ segmentation, image-guidance, motion tracking, quality assurance, and more. Despite this interest, practical clinical implementation of machine learning as part of the day-to-day clinical operations is still lagging. The aim of this white paper is to further promote progress in this new field of machine learning in radiation oncology by highlighting its untapped advantages and potentials for clinical advancement, while also presenting current challenges and open questions for future research. The targeted audience of this paper includes newcomers as well as practitioners in the field of medical physics/radiation oncology. The paper also provides general recommendations to avoid common pitfalls when applying these powerful data analytic tools to medical physics and radiation oncology problems and suggests some guidelines for transparent and informative reporting of machine learning results.

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Section: Introductionmentioning

confidence: 99%

“…Machine learning could be further subdivided per the nature of the data labeling into: supervised, unsupervised, and semi‐supervised . Supervised learning is used to estimate an unknown (input, output) mapping from known (input, output) samples, where the output is “labeled” (e.g., classification or regression).…”

Section: Introductionmentioning

confidence: 99%