用对比学习自动识别牛胚胎细胞阶段，提升精度与泛化能力。
Supervised contrastive learning for cell stage classification of animal embryos
- 结合监督对比学习与焦点损失，优化模型对模糊和不平衡数据的处理。
- 在牛胚胎和小鼠胚胎数据集上均超越现有方法，准确率显著提升。
- 适合生物育种、胚胎发育研究领域，尤其适用于低质量视频分析。
将视频显微镜与机器学习结合，为体外生产胚胎的早期发育研究提供新路径。然而，手动标注发育事件（尤其是细胞分裂）耗时且难以规模化。本文提出一种深度学习方法，自动分类2D时间序列显微视频中的胚胎细胞阶段，聚焦于牛胚胎发育分析，构建了牛胚胎细胞阶段（Bovine ECS）数据集。挑战包括：（1）图像质量低及牛胚胎暗细胞导致阶段识别困难；（2）发育阶段边界处类别模糊；（3）数据分布不均衡。为此，提出CLEmbryo方法，采用监督对比学习结合焦点损失训练，并使用轻量级3D神经网络CSN-50作为编码器。实验表明，该方法在自建牛胚胎数据集及公开的NYU小鼠胚胎数据集上均优于当前最优方法，具备良好泛化能力。
原文摘要 · Abstract (English)
Videomicroscopy, when combined with machine learning, offers a promising approach for studying the early development of in vitro produced (IVP) embryos. However, manually annotating developmental events, and more specifically cell divisions, is time-consuming for a biologist and cannot scale up for practical applications. We aim to automatically classify the cell stages of embryos from 2D time-lapse microscopy videos with a deep learning approach. We focus on the analysis of bovine embryonic development using video microscopy, as we are primarily interested in the application of cattle breeding, and we have created a Bovine Embryos Cell Stages (ECS) dataset. The challenges are three-fold: (1) low-quality images and bovine dark cells that make the identification of cell stages difficult, (2) class ambiguity at the boundaries of developmental stages, and (3) imbalanced data distribution. To address these challenges, we introduce CLEmbryo, a novel method that leverages supervised contrastive learning combined with focal loss for training, and the lightweight 3D neural network CSN-50 as an encoder. We also show that our method generalizes well. CLEmbryo outperforms state-of-the-art methods on both our Bovine ECS dataset and the publicly available NYU Mouse Embryos dataset.
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