Recent years have witnessed tremendous growth in the application of machine learning (ML) and deep learning (DL) techniques in medical physics. Embracing the current big data era, medical physicists equipped with these state‐of‐the‐art tools should be able to solve pressing problems in modern radiation oncology. Here, a review of the basic aspects involved in ML/DL model building, including data processing, model training, and validation for medical physics applications is presented and discussed. Machine learning can be categorized based on the underlying task into supervised learning, unsupervised learning, or reinforcement learning; each of these categories has its own input/output dataset characteristics and aims to solve different classes of problems in medical physics ranging from automation of processes to predictive analytics. It is recognized that data size requirements may vary depending on the specific medical physics application and the nature of the algorithms applied. Data processing, which is a crucial step for model stability and precision, should be performed before training the model. Deep learning as a subset of ML is able to learn multilevel representations from raw input data, eliminating the necessity for hand crafted features in classical ML. It can be thought of as an extension of the classical linear models but with multilayer (deep) structures and nonlinear activation functions. The logic of going “deeper" is related to learning complex data structures and its realization has been aided by recent advancements in parallel computing architectures and the development of more robust optimization methods for efficient training of these algorithms. Model validation is an essential part of ML/DL model building. Without it, the model being developed cannot be easily trusted to generalize to unseen data. Whenever applying ML/DL, one should keep in mind, according to Amara’s law, that humans may tend to overestimate the ability of a technology in the short term and underestimate its capability in the long term. To establish ML/DL role into standard clinical workflow, models considering balance between accuracy and interpretability should be developed. Machine learning/DL algorithms have potential in numerous radiation oncology applications, including automatizing mundane procedures, improving efficiency and safety of auto‐contouring, treatment planning, quality assurance, motion management, and outcome predictions. Medical physicists have been at the frontiers of technology translation into medicine and they ought to be prepared to embrace the inevitable role of ML/DL in the practice of radiation oncology and lead its clinical implementation.

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医学物理学家的机器和深度学习入门。

近年来，机器学习（ML）和深度学习（DL）技术在医学物理学中的应用取得了巨大的增长。迎接当前的大数据时代，配备了这些最新工具的医学物理学家应该能够解决现代放射肿瘤学中的紧迫问题。在这里，将介绍和讨论ML / DL模型构建所涉及的基本方面，包括数据处理，模型训练和医学物理应用验证。可以基于基础任务将机器学习分为监督学习，无监督学习或强化学习。这些类别中的每一个都有其自己的输入/输出数据集特征，旨在解决医学物理中不同类别的问题，从过程自动化到预测分析。公认的是，数据大小要求可能会根据特定的医学物理学应用和所应用算法的性质而有所不同。在训练模型之前，应执行数据处理（这是确保模型稳定性和精度的关键步骤）。深度学习作为ML的子集能够从原始输入数据中学习多层次表示，从而消除了经典ML中手工制作功能的必要性。可以将其视为经典线性模型的扩展，但具有多层（深）结构和非线性激活函数。深入研究的逻辑与学习复杂的数据结构有关，并行计算体系结构的最新进展以及为有效训练这些算法而开发的更健壮的优化方法的发展，为实现这一目标提供了帮助。模型验证是ML / DL模型构建的重要组成部分。没有它，就很难轻易信任正在开发的模型以将其推广到看不见的数据。根据Amara的定律，每当应用ML / DL时，都应该记住，人类可能会在短期内高估技术的能力，而在长期内低估技术的能力。为了在标准的临床工作流程中确立ML / DL的作用，应开发考虑准确性和可解释性之间平衡的模型。机器学习/ DL算法在许多放射肿瘤学应用中具有潜力，包括使平凡的程序自动化，提高自动轮廓的效率和安全性，治疗计划，质量保证，运动管理和结果预测。