1 Department of Radiology and Biomedical Imaging, University of California, San Francisco, 505 Parnassus Ave, L-352, San Francisco, CA 94143.
2 Department of Radiology, Thomas Jefferson University Hospitals, Philadelphia, PA.
AJR Am J Roentgenol. 2019 Jan;212(1):52-56. doi: 10.2214/AJR.18.20328. Epub 2018 Nov 7.
Machine learning has potential to play a key role across a variety of medical imaging applications. This review seeks to elucidate the ways in which machine learning can aid and enhance diagnosis, treatment, and follow-up in neurooncology.
Given the rapid pace of development in machine learning over the past several years, a basic proficiency of the key tenets and use cases in the field is critical to assessing potential opportunities and challenges of this exciting new technology.
机器学习在各种医学影像应用中具有重要作用。本综述旨在阐明机器学习如何辅助和增强神经肿瘤学的诊断、治疗和随访。
鉴于过去几年机器学习的快速发展，基本掌握该领域的关键原理和用例对于评估这项令人兴奋的新技术的潜在机遇和挑战至关重要。
