用解耦融合网络提升试管婴儿妊娠预测准确率
DeFusion: An Effective Decoupling Fusion Network for Multi-Modal Pregnancy Prediction
- 将胚胎图像与生育指标解耦为相关/无关信息，精细融合
- 在4046例数据上达到领先性能，比现有方法更优
- 适合辅助生殖领域研究者和临床医生参考
时序胚胎图像与父母生育指标对体外受精胚胎移植（IVF-ET）妊娠预测均具价值。但现有机器学习模型未能充分挖掘两模态间的互补信息。本文提出解耦融合网络DeFusion，通过解耦模块将不同模态信息分离为相关与无关部分，实现更精细融合。结合时空位置编码处理时序胚胎图像，并采用表格变压器提取生育指标信息。在南方医科大学收集的4046例新数据集上评估，实验表明本模型优于当前最优方法；在眼病预测数据集上的表现也验证了其良好泛化能力。代码已开源。
原文摘要 · Abstract (English)
Temporal embryo images and parental fertility table indicators are both valuable for pregnancy prediction in \textbf{in vitro fertilization embryo transfer} (IVF-ET). However, current machine learning models cannot make full use of the complementary information between the two modalities to improve pregnancy prediction performance. In this paper, we propose a Decoupling Fusion Network called DeFusion to effectively integrate the multi-modal information for IVF-ET pregnancy prediction. Specifically, we propose a decoupling fusion module that decouples the information from the different modalities into related and unrelated information, thereby achieving a more delicate fusion. And we fuse temporal embryo images with a spatial-temporal position encoding, and extract fertility table indicator information with a table transformer. To evaluate the effectiveness of our model, we use a new dataset including 4046 cases collected from Southern Medical University. The experiments show that our model outperforms state-of-the-art methods. Meanwhile, the performance on the eye disease prediction dataset reflects the model's good generalization. Our code is available at https://github.com/Ou-Young-1999/DFNet.
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