Infertility is a global health problem, and an increasing number of couples are seeking medical assistance to achieve reproduction, at least half of which are caused by men. The success rate of assisted reproductive technologies depends on sperm assessment, in which experts determine whether sperm can be used for reproduction based on morphology and motility of sperm. Previous sperm assessment studies with deep learning have used datasets comprising images that include only sperm heads, which cannot consider motility and other morphologies of sperm. Furthermore, the labels of the dataset are one-hot, which provides insufficient support for experts, because assessment results are inconsistent between experts, and they have no absolute answer. Therefore, we constructed the video dataset for sperm assessment whose videos include sperm head as well as neck and tail, and its labels were annotated with soft-label. Furthermore, we proposed the sperm assessment framework and the neural network, RoSTFine, for sperm video recognition. Experimental results showed that RoSTFine could improve the sperm assessment performances compared to existing video recognition models and focus strongly on important sperm parts (i.e., head and neck).

翻译：不孕不育是全球性健康问题，越来越多的夫妇寻求医疗帮助以实现生育，其中至少半数由男性因素导致。辅助生殖技术的成功率取决于精子评估——专家需根据精子的形态和活力判断其是否可用于生殖。以往基于深度学习的精子评估研究多使用仅包含精子头部的图像数据集，无法兼顾精子的活力及其他形态特征。此外，数据集标签采用独热编码形式，这难以充分支持专家判断，因为评估结果在专家间存在不一致性，且无绝对标准答案。为此，我们构建了精子评估视频数据集，其视频内容涵盖精子头部、颈部及尾部，并采用软标签进行标注。进一步地，我们提出了精子评估框架及专用于精子视频识别的神经网络RoSTFine。实验结果表明，相较于现有视频识别模型，RoSTFine能有效提升精子评估性能，并显著聚焦于关键精子部位（即头部和颈部）。