Behnood Gholami

Summary

Affiliation: Georgia Institute of Technology
Country: USA

Publications

  1. pmc A compressive sensing approach for glioma margin delineation using mass spectrometry
    Behnood Gholami
    Schools of Electrical and Computer and Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
    Conf Proc IEEE Eng Med Biol Soc 2011:5682-5. 2011
  2. pmc Agitation and pain assessment using digital imaging
    Behnood Gholami
    School of Aerospace Engineering, Georgia Institute of Technology, Atlanta, GA, 30332 0150, USA
    Conf Proc IEEE Eng Med Biol Soc 2009:2176-9. 2009
  3. pmc Relevance vector machine learning for neonate pain intensity assessment using digital imaging
    Behnood Gholami
    School of Aerospace Engineering, Georgia Institute of Technology, Atlanta, GA 30332 0150, USA
    IEEE Trans Biomed Eng 57:1457-66. 2010
  4. pmc Classification of astrocytomas and oligodendrogliomas from mass spectrometry data using sparse kernel machines
    Jacob Huang
    School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
    Conf Proc IEEE Eng Med Biol Soc 2011:7965-8. 2011

Detail Information

Publications4

  1. pmc A compressive sensing approach for glioma margin delineation using mass spectrometry
    Behnood Gholami
    Schools of Electrical and Computer and Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
    Conf Proc IEEE Eng Med Biol Soc 2011:5682-5. 2011
    ..In addition, our proposed framework is model-free, and hence, requires no prior information of spatial distribution of the tumor cell concentration...
  2. pmc Agitation and pain assessment using digital imaging
    Behnood Gholami
    School of Aerospace Engineering, Georgia Institute of Technology, Atlanta, GA, 30332 0150, USA
    Conf Proc IEEE Eng Med Biol Soc 2009:2176-9. 2009
    ..In this paper, we show that the pain intensity assessment given by a computer classifier has a strong correlation with the pain intensity assessed by expert and non-expert human examiners...
  3. pmc Relevance vector machine learning for neonate pain intensity assessment using digital imaging
    Behnood Gholami
    School of Aerospace Engineering, Georgia Institute of Technology, Atlanta, GA 30332 0150, USA
    IEEE Trans Biomed Eng 57:1457-66. 2010
    ..We also correlate our results with the pain intensity assessed by expert and nonexpert human examiners...
  4. pmc Classification of astrocytomas and oligodendrogliomas from mass spectrometry data using sparse kernel machines
    Jacob Huang
    School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
    Conf Proc IEEE Eng Med Biol Soc 2011:7965-8. 2011
    ..In this paper, we present a framework using sparse kernel machines to determine a glioma sample's histopathological subtype by analyzing its chemical composition acquired by desorption electrospray ionization mass spectrometry...