Deep Learning with Attention to Predict Gestational Age of the Fetal Brain
This work addresses the need for accurate gestational age assessment in prenatal screening, which is crucial for early diagnosis of congenital anomalies, representing a domain-specific incremental improvement.
The study tackled the problem of predicting gestational age from fetal brain MRI by developing an attention-based deep learning model, achieving an R² of 0.94 using multi-view MRI and attention mechanisms.
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.