Photo by hajjidirir from unsplash
The aims of this work are (1) to explore deep learning (DL) architectures, spectroscopic input types, and learning designs toward optimal quantification in MR spectroscopy of simulated pathological spectra; and… Click to show full abstract
The aims of this work are (1) to explore deep learning (DL) architectures, spectroscopic input types, and learning designs toward optimal quantification in MR spectroscopy of simulated pathological spectra; and (2) to demonstrate accuracy and precision of DL predictions in view of inherent bias toward the training distribution.
Keywords:
spectra deep;
quantification spectra;
learning;
learning idealized;
deep learning
Journal Title: Magnetic Resonance in Medicine
Year Published: 2022
Link to full text (if available)
Share on Social Media: Sign Up to like & get
recommendations!
Journal Title: Magnetic Resonance in Medicine
Year Published: 2022
Link to full text (if available)
Share on Social Media: Sign Up to like & get
recommendations!