摘要
该系列文章介绍了核医学背景下的机器学习(ML)。第一部分讨论了ML的历史,描述了常见的算法并举例说明ML何时可用于核医学。第二部分聚焦于ML在当下对核医学领域的贡献,讨论了未来对ML的预期和局限性,并对ML能做什么和不能做什么进行非常严谨的评估。
作者
陈文坤(译)
李雪娜
李亚明(审校)
Uribe F.Carlos;Sulantha Mathotaarachchi;Vincent Gaudet;Kenneth C.Smith;Pedro Rosa-Neto;François Bénard;Black E.Sandra;Katherine Zukotynski;Chen Wenkun;Li Xuena;Li Yaming(Department of Molecular Oncology,BC Cancer,Vancouver,British Columbia,Canada;Translational Neuroimaging Lab,McGill University,Montreal,Quebec,Canada;Department of Electrical and Computer Engineering,University of Waterloo,Waterloo,Ontario,Canada;Department of Electrical and Computer Engineering,University of Toronto,Toronto,Ontario,Canada;Department of Radiology,University of British Columbia,Vancouver,British Columbia,Canada;Department of Medicine(Neurology),Sunnybrook Health Sciences Centre,University of Toronto,Toronto,Ontario,Canada;Institute of Biomaterials and Biomedical Engineering,University of Toronto,Toronto,Ontario,Canada;Departments of Medicine and Radiology,McMaster University,Hamilton,Ontario,Canada;不详)
出处
《中华核医学与分子影像杂志》
CAS
CSCD
北大核心
2021年第8期501-506,共6页
Chinese Journal of Nuclear Medicine and Molecular Imaging
