# Embryo staging with weakly-supervised region selection and dynamically-decoded predictions

Source: [Embryo staging with weakly-supervised region selection and dynamically-decoded predictions](<https://www.sota2.com/research/paper/68fa65a31e209131cea0b78a>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T21:31:37.819Z

Authors: Tingfung Lau, Nathan Ng, Julian Gingold, Nina Desai, Julian McAuley, Zachary C. Lipton

Published: 2019-04-09T02:03:08.000Z

## Abstract

To optimize clinical outcomes, fertility clinics must strategically select which embryos to transfer. Common selection heuristics are formulas expressed in terms of the durations required to reach various developmental milestones, quantities historically annotated manually by experienced embryologists based on time-lapse EmbryoScope videos. We propose a new method for automatic embryo staging that exploits several sources of structure in this time-lapse data. First, noting that in each image the embryo occupies a small subregion, we jointly train a region proposal network with the downstream classifier to isolate the embryo. Notably, because we lack ground-truth bounding boxes, our we weakly supervise the region proposal network optimizing its parameters via reinforcement learning to improve the downstream classifier's loss. Moreover, noting that embryos reaching the blastocyst stage progress monotonically through earlier stages, we develop a dynamic-programming-based decoder that post-processes our predictions to select the most likely monotonic sequence of developmental stages. Our methods outperform vanilla residual networks and rival the best numbers in contemporary papers, as measured by both per-frame accuracy and transition prediction error, despite operating on smaller data than many.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Embryo Stage Classification on NYU Mouse Embryo 2 (test)](<https://www.sota2.com/research/sota/embryo-stage-classification-on-nyu-mouse-embryo-2-test.md>): Embryo Stage Classification; NYU Mouse Embryo 2 (test); Global Accuracy: 0.98.

- [Developmental Stage Classification on Human embryo dataset (test)](<https://www.sota2.com/research/sota/developmental-stage-classification-on-human-embryo-dataset-test.md>): Developmental Stage Classification; Human embryo dataset (test); Global Accuracy: 77.1.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1904.04419>)

[PDF](<https://arxiv.org/pdf/1904.04419.pdf>)