Billings John M, Eder Maxwell, Flood William C, Dhami Devendra Singh, Natarajan Sriraam, Whitlow Christopher T
Radiology Informatics and Image Processing Laboratory (RIIPL), Wake Forest School of Medicine, Medical Center Boulevard, Winston-Salem, NC 27157, USA; Division of Neuroradiology, Department of Radiology, Wake Forest School of Medicine, Medical Center Boulevard, Winston-Salem, NC 27157, USA.
School of Informatics and Computing, Indiana University, Informatics East Building, Room 257, 919 E. 10th Street, Bloomington, IN 47408, USA.
Neuroimaging Clin N Am. 2017 Nov;27(4):609-620. doi: 10.1016/j.nic.2017.06.010.
Machine learning is one of the most exciting and rapidly expanding fields within computer science. Academic and commercial research entities are investing in machine learning methods, especially in personalized medicine via patient-level classification. There is great promise that machine learning methods combined with resting state functional MR imaging will aid in diagnosis of disease and guide potential treatment for conditions thought to be impossible to identify based on imaging alone, such as psychiatric disorders. We discuss machine learning methods and explore recent advances.
机器学习是计算机科学中最令人兴奋且发展迅速的领域之一。学术和商业研究机构都在投资机器学习方法，尤其是通过患者层面的分类用于个性化医疗。机器学习方法与静息态功能磁共振成像相结合，极有可能有助于疾病诊断，并为那些仅靠影像学认为无法识别的病症（如精神疾病）指导潜在的治疗方案。我们将讨论机器学习方法并探索其最新进展。
