Improved self-calibrated signal model for χ-separation using single-subject (N=1) physics-constrained deep learning with histological validation.


Conference


Benslimane I, Grabner G, Hametner S, Jochmann T, Zivadinov R, Schweser F
Artificial Intelligence in Clinical Imaging (AICI) Forum, Graz, Austria, 2022

Cite

Cite

APA   Click to copy
I, B., G, G., S, H., T, J., R, Z., & F, S. (2022). Improved self-calibrated signal model for χ-separation using single-subject (N=1) physics-constrained deep learning with histological validation. . Graz, Austria.


Chicago/Turabian   Click to copy
I, Benslimane, Grabner G, Hametner S, Jochmann T, Zivadinov R, and Schweser F. “Improved Self-Calibrated Signal Model for χ-Separation Using Single-Subject (N=1) Physics-Constrained Deep Learning with Histological Validation. .” Artificial Intelligence in Clinical Imaging (AICI) Forum. Graz, Austria, 2022.


MLA   Click to copy
I, Benslimane, et al. Improved Self-Calibrated Signal Model for χ-Separation Using Single-Subject (N=1) Physics-Constrained Deep Learning with Histological Validation. . 2022.


BibTeX   Click to copy

@conference{benslimane2022a,
  title = {Improved self-calibrated signal model for χ-separation using single-subject (N=1) physics-constrained deep learning with histological validation. },
  year = {2022},
  address = {Graz, Austria},
  series = {Artificial Intelligence in Clinical Imaging (AICI) Forum},
  author = {I, Benslimane and G, Grabner and S, Hametner and T, Jochmann and R, Zivadinov and F, Schweser}
}





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