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.
Cita Chicago Style (17a ed.)Park, Ji Eun, Ho Sung Kim, Junkyu Lee, E.-Nae Cheong, Ilah Shin, Sung Soo Ahn, y 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.
Cita MLA (8a ed.)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.