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Abstract #1099

Deep Learning with Attention to Predict Gestational Age of the Fetal Brain Using MRI

Liyue Shen1, Katie Shpanskaya2, Edward Lee1, Emily McKenna2, Maryam Maleki2, Quin Lu3, John Pauly1, and Kristen Yeom2

1Electrical Engineering, Stanford University, Stanford, CA, United States, 2Radiology, Stanford University, Stanford, CA, United States, 3Philips Healthcare North America, Gainesville, FL, United States

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 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 uses age-activated weakly-supervised attention maps to enable rotation-invariant localization of fetal brain among background noise. We evaluate our method on a collected fetal brain MRI cohort and achieve promising age prediction performance.

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