An initial experience of machine learning based on multi-sequence texture parameters in magnetic resonance imaging to differentiate glioblastoma from brain metastases.

MLA

Tateishi, Machiko, et al. “An Initial Experience of Machine Learning Based on Multi-Sequence Texture Parameters in Magnetic Resonance Imaging to Differentiate Glioblastoma from Brain Metastases.” Journal of the Neurological Sciences, vol. 410, Mar. 2020, p. N.PAG. EBSCOhost, https://doi.org/10.1016/j.jns.2019.116514.

APA

Tateishi, M., Nakaura, T., Kitajima, M., Uetani, H., Nakagawa, M., Inoue, T., Kuroda, J., Mukasa, A., & Yamashita, Y. (2020). An initial experience of machine learning based on multi-sequence texture parameters in magnetic resonance imaging to differentiate glioblastoma from brain metastases. Journal of the Neurological Sciences, 410, N.PAG. https://doi.org/10.1016/j.jns.2019.116514

Chicago

Tateishi, Machiko, Takeshi Nakaura, Mika Kitajima, Hiroyuki Uetani, Masataka Nakagawa, Taihei Inoue, Jun-ichiro Kuroda, Akitake Mukasa, and Yasuyuki Yamashita. 2020. “An Initial Experience of Machine Learning Based on Multi-Sequence Texture Parameters in Magnetic Resonance Imaging to Differentiate Glioblastoma from Brain Metastases.” Journal of the Neurological Sciences 410 (March): N.PAG. doi:10.1016/j.jns.2019.116514.