APA (7th ed.) Citation

Park, J. E., Kim, H. S., Lee, J., Cheong, E., Shin, I., Ahn, S. S., & Shim, W. H. (2020). Deep-learned time-signal intensity pattern analysis using an autoencoder captures magnetic resonance perfusion heterogeneity for brain tumor differentiation. Nature Portfolio.

Chicago Style (17th ed.) Citation

Park, Ji Eun, Ho Sung Kim, Junkyu Lee, E.-Nae Cheong, Ilah Shin, Sung Soo Ahn, and Woo Hyun Shim. Deep-learned Time-signal Intensity Pattern Analysis Using an Autoencoder Captures Magnetic Resonance Perfusion Heterogeneity for Brain Tumor Differentiation. Nature Portfolio, 2020.

MLA (8th ed.) Citation

Park, Ji Eun, et al. Deep-learned Time-signal Intensity Pattern Analysis Using an Autoencoder Captures Magnetic Resonance Perfusion Heterogeneity for Brain Tumor Differentiation. Nature Portfolio, 2020.