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Introduction to machine and deep learning for medical physicists.

Sunan Cui1,2, Huan-Hsin Tseng1, Julia Pakela1,2

  • 1Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, 48103, USA.

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Machine learning (ML) and deep learning (DL) offer powerful tools for medical physics, enabling solutions in radiation oncology. This review covers ML/DL model building, data processing, training, and validation for clinical applications.

Keywords:
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Area of Science:

  • Medical Physics
  • Machine Learning
  • Deep Learning
  • Radiation Oncology

Background:

  • The integration of machine learning (ML) and deep learning (DL) is rapidly advancing medical physics.
  • These technologies are crucial for addressing complex challenges in the current big data era of radiation oncology.
  • Medical physicists are key to translating these advanced computational tools into clinical practice.

Purpose of the Study:

  • To review the fundamental aspects of building ML/DL models for medical physics applications.
  • To discuss data processing, model training, and validation strategies.
  • To highlight the potential of ML/DL in radiation oncology workflows.

Main Methods:

  • Categorization of ML into supervised, unsupervised, and reinforcement learning based on task.
  • Discussion of data processing requirements, model training methodologies, and validation techniques.
  • Explanation of DL as a subset of ML, emphasizing its ability to learn from raw data without feature engineering.

Main Results:

  • ML/DL algorithms can automate tasks, improve auto-contouring, aid treatment planning, and enhance quality assurance.
  • Potential for improved motion management and outcome prediction in radiation oncology.
  • The need for models balancing accuracy and interpretability for clinical integration.

Conclusions:

  • ML/DL techniques hold significant promise for revolutionizing radiation oncology.
  • Medical physicists must prepare to lead the clinical implementation of these technologies.
  • Careful consideration of Amara's law is needed to manage expectations regarding technology adoption.