# Early prediction of the transferability of bovine embryos from videomicroscopy

arXiv: https://arxiv.org/abs/2501.07945 · HTML: https://mlsanity.com/paper/2501.07945/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 23 references that are in the map.
- First posted on arXiv 14 Jan 2025.

## Builds on (most cited first, 15 of 15)

- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [Deformable DETR: Deformable Transformers for End-to-End Object Detection](https://mlsanity.com/paper/2010.04159/) (2020)
- [Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset](https://mlsanity.com/paper/1705.07750/) (2017)
- [SlowFast Networks for Video Recognition](https://mlsanity.com/paper/1812.03982/) (2018)
- [Group Normalization](https://mlsanity.com/paper/1803.08494/) (2018)
- [Averaging Weights Leads to Wider Optima and Better Generalization](https://mlsanity.com/paper/1803.05407/) (2018)
- [A Closer Look at Spatiotemporal Convolutions for Action Recognition](https://mlsanity.com/paper/1711.11248/) (2017)
- [Two-Stream Convolutional Networks for Action Recognition in Videos](https://mlsanity.com/paper/1406.2199/) (2014)
- [An Overview of Multi-Task Learning in Deep Neural Networks](https://mlsanity.com/paper/1706.05098/) (2017)
- [Cyclical Learning Rates for Training Neural Networks](https://mlsanity.com/paper/1506.01186/) (2015)
- [Self-Supervised Learning for Videos: A Survey](https://mlsanity.com/paper/2207.00419/) (2022)
- [Negative Log Likelihood Ratio Loss for Deep Neural Network Classification](https://mlsanity.com/paper/1804.10690/) (2018)
- [EmbryosFormer: Deformable Transformer and Collaborative Encoding-Decoding for Embryos Stage Development Classification](https://mlsanity.com/paper/2210.04615/) (2022)
- [Learning Vector Quantized Shape Code for Amodal Blastomere Instance Segmentation](https://mlsanity.com/paper/2012.00985/) (2020)
- [Robust and generalizable embryo selection based on artificial intelligence and time-lapse image sequences](https://mlsanity.com/paper/2103.07262/) (2021)

## Built on by (most cited first, 2 of 2)

- [Supervised contrastive learning for cell stage classification of animal embryos](https://mlsanity.com/paper/2502.07360/) (2025)
- [From Division to Decision: Leveraging Temporal Cell-Stage Segmentation for Embryo Transferability Prediction](https://mlsanity.com/paper/2605.18923/) (2026)

## Abstract (arXiv)

Videomicroscopy is a promising tool combined with machine learning for studying the early development of in vitro fertilized bovine embryos and assessing its transferability as soon as possible. We aim to predict the embryo transferability within four days at most, taking 2D time-lapse microscopy videos as input. We formulate this problem as a supervised binary classification problem for the classes transferable and not transferable. The challenges are three-fold: 1) poorly discriminating appearance and motion, 2) class ambiguity, 3) small amount of annotated data. We propose a 3D convolutional neural network involving three pathways, which makes it multi-scale in time and able to handle appearance and motion in different ways. For training, we retain the focal loss. Our model, named SFR, compares favorably to other methods. Experiments demonstrate its effectiveness and accuracy for our challenging biological task.