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AI Research
arXiv CS.AI·Jun 9·5 min read
AttnRegDeepLab: A Two-Stage Decoupled Framework for Interpretable Embryo Fragmentation Grading
TL;DR
Assessing embryo fragmentation is essential for predicting the success of in vitro fertilization (IVF), but manual grading is subjective and existing AI models lack interpretability. AttnRegDeepLab is a Multi-Task Learning framework that enhances the DeepLabV3+ model with Attention Gates to improve segmentation accuracy.
✦ Why It Matters
Engineers can apply AttnRegDeepLab to enhance AI interpretability in medical imaging tasks.
Key Takeaways
- Integrate Attention Gates to enhance segmentation quality.
- Use Multi-Scale Regression Head for improved area estimation.
- Adopt a two-stage training strategy to resolve gradient conflicts.
- Implement range-based loss for weakly labeled data.
How It Works
AttnRegDeepLab enhances the DeepLabV3+ model by adding Attention Gates, which focus on relevant features while filtering out noise. This allows for sharper contour detection in embryo images.
The Multi-Scale Regression Head guides the segmentation process by incorporating global grading information, which helps in reducing systematic errors in area estimation. The two-stage decoupled training approach allows the model to learn effectively from weakly labeled data, minimizing conflicts that typically arise in Multi-Task Learning scenarios.