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- W2905580016 abstract "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." @default.
- W2905580016 created "2018-12-22" @default.
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- W2905580016 date "2018-12-09" @default.
- W2905580016 modified "2023-09-25" @default.
- W2905580016 title "Deep Learning with Attention to Predict Gestational Age of the Fetal Brain" @default.
- W2905580016 cites W2786052267 @default.
- W2905580016 doi "https://doi.org/10.48550/arxiv.1812.07102" @default.
- W2905580016 hasPublicationYear "2018" @default.
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