# Early prediction of the transferability of bovine embryos from videomicroscopy

Yasmine Hachani, Patrick Bouthemy, Sylvie Ruffini, Ludivine Laffont, Elisa Fromont, Alline de Paula Reis

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## To cite this version:

Yasmine Hachani, Patrick Bouthemy, Sylvie Ruffini, Ludivine Laffont, Elisa Fromont, et al. Early prediction of the transferability of bovine embryos from videomicroscopy. IABM 2024 - Colloque Français d'Intelligence Artificielle en Imagerie Biomédicale, Mar 2024, Grenoble, France. pp.1-1, 2024. hal-04880124

HAL Id: hal-04880124

https://hal.science/hal-04880124v1

Submitted on 10 Jan 2025

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Distributed under a Creative Commons Attribution 4.0 International License
# EARLY PREDICTION OF THE TRANSFERABILITY OF BOVINE EMBRYOS
## FROM VIDEOMICROSCOPY

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Y. Hachani¹, P. Bouthemy¹, S. Ruffini⁴, L. Laffont⁴, E. Fromont¹,², A. De Paula Reis³,⁴

¹Inria Rennes,²Univ Rennes, IUF, IRISA,³Ecole Nationale Vétérinaire d'Alfort,⁴INRAE, BREED

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UMR

IRISA

## OVERVIEW

We aim to predict **early and automatically** the transferability of embryos for biological issues and cattle breeding challenges, taking as input **2D time-lapse videos** up to the fourth embryonic development day. We propose:

* a formulation as a **supervised classification** with two classes **Transferable (T)** and **Non Transferable (NT)**,

* a **3D-CNN** with **three pathways**, multi-scale in time and able to handle appearance and motion in different ways,

* the use of the **focal loss** for training.

Our model SFR compares favorably to other methods and demonstrates its accuracy and efficiency for our challenging biological task.

## DATA AND METHODOLOGICAL CHALLENGES

This problem is challenging for multiple reasons:

* Complex morphological and temporal processes

* Noisy, poorly contrasted videos with transparency effects and inter-twined appearance and motion

* High intra-class variability and low inter-class distance

* Small set of 900 annotated videos

* Light annotation with only the class label per video

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class T

class NT

<caption>**Fig. 1:** Two sample videos of IVF bovine embryos, with three images taken at distant time instants. The bovine embryo (dark grey), surrounded by the zona pellucida, is located in a micro-well (light grey) within the Petri dish (black).</caption>

## OUR NETWORK CALLED SFR

Inspired by SlowFast [1], we have designed a 3D-CNN with three pathways handling appearance and motion in complementary ways:

* *Slow* pathway mainly dedicated to capture spatial features,

* *Fast* pathway mainly devoted to temporal features,

* *Regular* pathway combining appearance and motion.

All options were experimentally validated in an ablation study.

<figure><img src="image_7.png" /><figcaption>**Fig. 2:** Our model called SFR involves three pathways, *Slow*, *Fast*, and *Regular*, all implemented with 3D ResNet18 of different configurations. Input video is given at a $\alpha$ times lower temporal rate for the *Slow* pathway. The *Fast* pathway has only a fraction $\beta$ of the channels. The three outputs are combined before providing the prediction. Directed lateral connections are included between the pathways.</figcaption></figure>

## FOCAL LOSS

We use the focal loss [2] instead of the cross entropy loss

* to address imbalance between the two classes,

* to focus the training of the model on hard examples,

* to improve performance (validated by the ablation study).

The focal loss writes:

$$ \mathcal{L}_f(v, y) = - \sum_{c=1}^{2} \eta_c (1 - \hat{p}(y_c|v))^{\gamma} p(y_c|v) \log \hat{p}(y_c|v), $$

with $v$ the input video, $y_c$ label of one of the two classes, $\hat{p}(y_c|v)$ the predicted probability of having class $c$ given video $v$, $p(y_c|v)$ the true one (equal to 1 here due to supervised learning), $\eta_c$ the weight for class $c$, and $\gamma$ the focusing parameter.

## IMPLEMENTATION DETAILS

* Use AdamW optimizer with a learning rate of $10^{-4}$ and a cyclic learning rate scheduler,

* Set loss parameters to $\gamma = 2$, $\eta_1 = 1.25$, and $\eta_2 = 0.833$,

* Apply stochastic weight averaging technique [3].

## EXPERIMENTAL RESULTS

<table><caption>**Table 1:** Accuracy, Precision and Recall, for the two classes *T* and *NT*, obtained with respectively 2D-Resnet18+GRU, 3D-Resnet18, SlowFast [1], and our SFR model, all the models with the focal loss ($\gamma = 2$).</caption><thead><tr><th>Model</th><th>Acc</th><th>P<sub>T</sub></th><th>R<sub>T</sub></th><th>P<sub>NT</sub></th><th>R<sub>NT</sub></th></tr></thead><tbody><tr><td>2D-Resnet18+GRU</td><td>67.6±4.1</td><td>54.6±5.6</td><td>48.4±19.9</td><td>74.2±5.9</td><td>78.3±8.6</td></tr><tr><td>3D-Resnet18(FL)</td><td>74.6±4.0</td><td>64.1±6.5</td><td><strong>67.4±12.4</strong></td><td><strong>82.0±4.6</strong></td><td>78.6±7.8</td></tr><tr><td>SlowFast(FL)</td><td>73.1±2.6</td><td>63.9±4.0</td><td>55.2±9.3</td><td>77.3±3.2</td><td>82.8±3.4</td></tr><tr><td>Ours - SFR(FL)</td><td><strong>75.6±1.5</strong></td><td><strong>66.8±3.3</strong></td><td>62.5±3.4</td><td>80.1±1.1</td><td><strong>82.8±2.9</strong></td></tr></tbody></table>

* Our SFR method provides the overall best performance,

* Biologists prioritize stability and our model is the most stable one,

* Key metrics are $P_T$ and $R_{NT}$ regarding the application needs, and our method provides the best scores for them.

## ACKNOWLEDGEMENTS

The research to produce the original data was funded by CRB-Anim and APIS-GENE.

## REFERENCES

[1] C. Feichtenhofer, H. Fan, J. Malik, and K. He. SlowFast networks for video recognition. ICCV 2019.

[2] T.-Y. Lin, P. Goyal, R. Girshick, K. He, and P. Dollár. Focal loss for dense object detection. ICCV 2017.

[3] P. Izmailov, D. Podoprikhin, T. Garipov, D. Vetrov, and A. Gordon Wilson. Averaging weights leads to wider optima and better generalization. UAI 2018.