用视频预测牛胚胎移植成功率，4天内完成。
Early prediction of the transferability of bovine embryos from videomicroscopy
- 设计三通道3D CNN，多尺度分析胚胎形态与运动
- 在小样本标注下准确区分可移植与不可移植胚胎
- 适合辅助畜牧业早期胚胎筛选，提升繁殖效率
视频显微镜结合机器学习可早期研究体外受精牛胚胎发育并尽早评估其移植潜力。本文旨在最多四天内，以二维时间序列显微视频为输入，预测胚胎是否具备移植能力。该问题被建模为二分类任务，分为可移植和不可移植两类。挑战包括：1）形态与运动特征区分度低；2）类别间存在模糊性；3）标注数据量少。为此，提出一种含三个路径的3D卷积神经网络，实现时间上的多尺度建模，并分别处理外观与运动信息。训练中采用焦点损失（focal loss）。所提模型名为SFR，性能优于其他方法。实验验证了其在该复杂生物任务中的有效性与准确性。
原文摘要 · Abstract (English)
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.
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