Liyue Shen is a final-year Ph.D. candidate in Electrical Engineering at Stanford University, co-advised by John Pauly and Lei Xing. Her research focuses on the interdisciplinary field of Medical AI to develop efficient AI/ML-driven computational algorithms and techniques for biomedical imaging and processing to address real-world biomedical and healthcare problems. Her works have been published in both ML/CV conferences (ICLR, ICCV, CVPR) and medical journals (Nature BME, IEEE TMI, MedIa, Scientific Reports). She was the recipient of the Stanford Bio-X Bowes Graduate Student Fellowship, and was selected as Rising Star in EECS by MIT and Rising Star in Data Science by University of Chicago. She co-organized the Women in Machine Learning (WiML) Workshop at ICML 2021 and the Machine Learning for Healthcare (ML4H) Workshop at NeurIPS 2021. She received an M.Sc. from Stanford University. Before that, she conferred her bachelor’s degree in Electronic Engineering from Tsinghua University.
Magnetic resonance imaging offers unrivaled visualization of the fetal brain, forming the basis for establishing age-specific morphologic milestones. However, gauging age-appropriate neural development remains a difficult task due to the constantly changing appearance of the fetal brain, variable image quality, and frequent motion artifacts. Here we present an end-to-end, attention-guided deep learning model that predicts gestational age with R2 score of 0.945, mean absolute error of 6.7 days, and concordance correlation coefficient of 0.970. The convolutional neural network was trained on a heterogeneous dataset of 741 developmentally normal fetal brain images ranging from 19 to 39 weeks in gestational age. We also demonstrate model performance and generalizability using independent datasets from four academic institutions across the U.S. and Turkey with R2 scores of 0.81 to 0.90 after minimal fine-tuning. The proposed regression algorithm provides an automated machine-enabled tool with the potential to better characterize in utero neurodevelopment and guide real-time gestational age estimation after the first trimester.