# From Division to Decision: Leveraging Temporal Cell-Stage Segmentation for Embryo Transferability Prediction

arXiv: https://arxiv.org/abs/2605.18923 · HTML: https://mlsanity.com/paper/2605.18923/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 22 references that are in the map.
- First posted on arXiv 18 May 2026.

## Builds on (most cited first, 11 of 11)

- [Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset](https://mlsanity.com/paper/1705.07750/) (2017)
- [Temporal Convolutional Networks for Action Segmentation and Detection](https://mlsanity.com/paper/1611.05267/) (2016)
- [MS-TCN: Multi-Stage Temporal Convolutional Network for Action Segmentation](https://mlsanity.com/paper/1903.01945/) (2019)
- [Unified Fully and Timestamp Supervised Temporal Action Segmentation via Sequence to Sequence Translation](https://mlsanity.com/paper/2209.00638/) (2022)
- [A Hybrid RNN-HMM Approach for Weakly Supervised Temporal Action Segmentation](https://mlsanity.com/paper/1906.01028/) (2019)
- [C2F-TCN: A Framework for Semi and Fully Supervised Temporal Action Segmentation](https://mlsanity.com/paper/2212.11078/) (2022)
- [Permutation-Aware Action Segmentation via Unsupervised Frame-to-Segment Alignment](https://mlsanity.com/paper/2305.19478/) (2023)
- [EmbryosFormer: Deformable Transformer and Collaborative Encoding-Decoding for Embryos Stage Development Classification](https://mlsanity.com/paper/2210.04615/) (2022)
- [Robust and generalizable embryo selection based on artificial intelligence and time-lapse image sequences](https://mlsanity.com/paper/2103.07262/) (2021)
- [Developmental Stage Classification of Embryos Using Two-Stream Neural Network with Linear-Chain Conditional Random Field](https://mlsanity.com/paper/2107.06360/) (2021)
- [Early prediction of the transferability of bovine embryos from videomicroscopy](https://mlsanity.com/paper/2501.07945/) (2025)

## Abstract (arXiv)

Accurate selection of bovine embryos is a challenging task, as current practice relies on a single expert assessment on the seventh day after insemination, resulting in high rates of pregnancy loss. Time-lapse videomicroscopy provides detailed information on early development, but is difficult to exploit because of complex motion patterns and time-consuming analysis. We propose TransFACT, a transformer-based framework for modeling early developmental stages and embryo transferability using 2D time-lapse videos from the first four days of development. TransFACT combines frame-level temporal features with stage-level representations, using developmental stages as auxiliary supervision to predict transferability on day four. Our experiments demonstrate that TransFACT, by leveraging an existing method designed for action recognition, achieves superior performance than its competitor in predicting embryo transferability.