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：不育症是一个全球性健康问题，越来越多的夫妇寻求医疗辅助以实现生育，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，专家需根据精子形态与活力判定其是否可用于生殖。以往基于深度学习的精子评估研究使用的数据集仅包含精子头部的图像，无法兼顾精子活力及其他形态特征。此外，数据集的标签为独热编码，无法为专家提供充分支持——因为专家间的评估结果存在不一致性，且此类评估并无绝对答案。为此，我们构建了精子评估视频数据集，其中视频不仅包含精子头部，还涵盖颈部与尾部，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面向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能显著提升精子评估性能，并高度聚焦于精子关键部位（即头部与颈部）。