A shared ResNet-50 multi-task network jointly segments cytoplasmic fragmentation (Dice 0.781) and classifies t2/t4 stage and blastomere symmetry on 9,137 cleavage-stage embryo images.
Development of deep learning algorithms for predicting blastocyst formation and quality by time -lapse monitoring
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EMBRACE: A Multi-task Framework for Comprehensive Quality Assessment in Cleavage-stage Embryo
A shared ResNet-50 multi-task network jointly segments cytoplasmic fragmentation (Dice 0.781) and classifies t2/t4 stage and blastomere symmetry on 9,137 cleavage-stage embryo images.