Deep-learned time-signal intensity pattern analysis using an autoencoder captures magnetic resonance perfusion heterogeneity for brain tumor differentiation

MLA

Ilah Shin, et al. “Deep-Learned Time-Signal Intensity Pattern Analysis Using an Autoencoder Captures Magnetic Resonance Perfusion Heterogeneity for Brain Tumor Differentiation.” Scientific Reports, vol. 10, Dec. 2020. EBSCOhost, https://doi.org/10.1038/s41598-020-78485-x.

APA

Ilah Shin, Sung Soo Ahn, Jun-Kyu Lee, Woo Hyun Shim, Ho Sung Kim, E-Nae Cheong, & Ji Eun Park. (2020). Deep-learned time-signal intensity pattern analysis using an autoencoder captures magnetic resonance perfusion heterogeneity for brain tumor differentiation. Scientific Reports, 10. https://doi.org/10.1038/s41598-020-78485-x

Chicago

Ilah Shin, Sung Soo Ahn, Jun-Kyu Lee, Woo Hyun Shim, Ho Sung Kim, E-Nae Cheong, and Ji Eun Park. 2020. “Deep-Learned Time-Signal Intensity Pattern Analysis Using an Autoencoder Captures Magnetic Resonance Perfusion Heterogeneity for Brain Tumor Differentiation.” Scientific Reports 10 (December). doi:10.1038/s41598-020-78485-x.