Interpretable artificial intelligence in radiology and radiation oncology

被引:5
作者
Cui, Sunan [1 ]
Traverso, Alberto [2 ]
Niraula, Dipesh [3 ]
Zou, Jiaren [4 ]
Luo, Yi [3 ]
Owen, Dawn [5 ]
El Naqa, Issam [3 ]
Wei, Lise [4 ]
机构
[1] Univ Washington, Dept Radiat Oncol, Seattle, WA USA
[2] Dept Radiotherapy, Maastro Clin, Maastricht, Netherlands
[3] H Lee Moffitt Canc Ctr & Res Inst, Dept Machine Learning, Tampa, FL USA
[4] Univ Michigan, Dept Radiat Oncol, Ann Arbor, MI 48109 USA
[5] Mayo Clin, Dept Radiat Oncol, Rochester, MN USA
关键词
PREDICTION; CARE;
D O I
10.1259/bjr.20230142
中图分类号
R8 [特种医学]; R445 [影像诊断学];
学科分类号
1002 ; 100207 ; 1009 ;
摘要
Artificial intelligence has been introduced to clinical practice, especially radiology and radiation oncology, from image segmentation, diagnosis, treatment planning and prognosis. It is not only crucial to have an accurate artificial intelligence model, but also to understand the internal logic and gain the trust of the experts. This review is intended to provide some insights into core concepts of the interpretability, the state- of- the- art methods for understanding the machine learning models, the evaluation of these methods, identifying some challenges and limits of them, and gives some examples of medical applications.
引用
收藏
页数:10
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