Machine Learning & Radiation Oncology, Moffitt Cancer Center, 12902 Magnolia Drive, Tampa, FL, 33612, USA.
Department of Radiology, University of California Davis Health, Sacramento, CA, 95817, USA.
Med Phys. 2021 Sep;48(9):4711-4714. doi: 10.1002/mp.15170.
The Abstract is intended to provide a concise summary of the study and its scientific findings. For AI/ML applications in medical physics, a problem statement and rationale for utilizing these algorithms are necessary while highlighting the novelty of the approach. A brief numerical description of how the data are partitioned into subsets for training of the AI/ML algorithm, validation (including tuning of parameters), and independent testing of algorithm performance is required. This is to be followed by a summary of the results and statistical metrics that quantify the performance of the AI/ML algorithm.
摘要旨在提供研究及其科学发现的简明总结。对于医学物理学中的 AI/ML 应用，需要提出问题陈述和利用这些算法的基本原理，同时突出该方法的新颖性。简要描述如何将数据划分为子集，以便对 AI/ML 算法进行训练、验证（包括参数调整）和独立测试算法性能。接下来是对 AI/ML 算法性能进行量化的结果和统计指标的总结。
