  Fetal brain imaging is a cornerstone of prenatal screening and early
diagnosis of congenital anomalies. Knowledge of fetal gestational age is the
key to the accurate assessment of brain development. This study develops an
attention-based deep learning model to predict gestational age of the fetal
brain. The proposed model is an end-to-end framework that combines key insights
from multi-view MRI including axial, coronal, and sagittal views. The model
also uses age-activated weakly-supervised attention maps to enable
rotation-invariant localization of the fetal brain among background noise. We
evaluate our methods on the collected fetal brain MRI cohort with a large age
distribution from 125 to 273 days. Our extensive experiments show age
prediction performance with R2 = 0.94 using multi-view MRI and attention.
