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Predicting Embryo Morphokinetics in Videos with Late Fusion Nets & Dynamic Decoders
Nathan H Ng, Julian McAuley, Julian A Gingold, Nina Desai, Zachary C Lipton
Feb 12, 2018 (modified: Feb 13, 2018)ICLR 2018 Workshop Submissionreaders: everyone
Abstract:To optimize clinical outcomes, many fertility clinics select embryos strategically, based on how quickly they reach certain developmental milestones. This requires manually annotating time-lapse EmbryoScope videos with their corresponding morphokinetics, a time-consuming process that requires experienced embryologists. We propose late-fusion ConvNets with a dynamic programming-based decoder for automatically labeling these videos. Experiments address data extracted from EmbryoScope incubators at the Cleveland Clinic Foundation Fertility Center. We focus on 6 stages, demonstrating 87% per-frame accuracy.
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