Bradley J Erickson
Department of Radiology, Mayo Clinic, Mayo Building East 2, 200 First Street Southwest, Rochester, MN 55905, USA.
Radiol Clin North Am. 2021 Nov;59(6):933-940. doi: 10.1016/j.rcl.2021.06.004.
Machine learning is an important tool for extracting information from medical images. Deep learning has made this more efficient by not requiring an explicit feature extraction step and in some cases detecting features that humans had not identified. The rapid advance of deep learning technologies continues to result in valuable tools. The most effective use of these tools will occur when developers also understand the properties of medical images and the clinical questions at hand. The performance metrics also are critical for guiding the training of an artificial intelligence and for assessing and comparing its tools.
机器学习是从医学图像中提取信息的重要工具。深度学习通过不需要显式特征提取步骤，并且在某些情况下可以检测到人类尚未识别的特征，从而使这一过程更加高效。深度学习技术的快速发展不断产生有价值的工具。当开发人员也了解医学图像的特性和当前的临床问题时，这些工具将得到最有效的利用。性能指标对于指导人工智能的训练以及评估和比较其工具也至关重要。
