Machine Learning Applications to Resting-State Functional MR Imaging Analysis.

Journal: Neuroimaging clinics of North America
Published Date: Nov 1, 2017

Abstract

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.

Authors

  • John M Billings
    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.
  • Maxwell Eder
    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.
  • William C Flood
    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.
  • Devendra Singh Dhami
    School of Informatics and Computing, Indiana University, Informatics East Building, Room 257, 919 E. 10th Street, Bloomington, IN 47408, USA.
  • Sriraam Natarajan
    Department of Computer Science, University of Texas at Dallas, Richardson, Texas.
  • Christopher T Whitlow
    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; Department of Biomedical Engineering, Wake Forest School of Medicine, Medical Center Boulevard, Winston-Salem, NC 27157, USA; Clinical and Translational Sciences Institute (CTSI), Wake Forest School of Medicine, Medical Center Boulevard, Winston-Salem, NC 27157, USA. Electronic address: cwhitlow@wakehealth.edu.

Keywords

External Resources

Popular Topics
Recent Journals