Male infertility accounts for about one-third of global infertility cases. Manual assessment of sperm abnormalities through head morphology analysis encounters issues of observer variability and diagnostic discrepancies among experts. Its alternative, Computer-Assisted Semen Analysis (CASA), suffers from low-quality sperm images, small datasets, and noisy class labels. We propose a new approach for sperm head morphology classification, called SHMC-Net, which uses segmentation masks of sperm heads to guide the morphology classification of sperm images. SHMC-Net generates reliable segmentation masks using image priors, refines object boundaries with an efficient graph-based method, and trains an image network with sperm head crops and a mask network with the corresponding masks. In the intermediate stages of the networks, image and mask features are fused with a fusion scheme to better learn morphological features. To handle noisy class labels and regularize training on small datasets, SHMC-Net applies Soft Mixup to combine mixup augmentation and a loss function. We achieve state-of-the-art results on SCIAN and HuSHeM datasets, outperforming methods that use additional pre-training or costly ensembling techniques.

翻译：摘要：男性不育约占全球不育病例的三分之一。通过头部形态分析进行精子异常的手动评估存在观察者变异和专家间诊断差异的问题。其替代方案——计算机辅助精液分析（CASA）则面临精子图像质量低、数据集小以及类别标签噪声大的挑战。我们提出了一种新的精子头部形态分类方法，称为SHMC-Net，该方法利用精子头部的分割掩膜来引导精子图像的形态分类。SHMC-Net通过图像先验生成可靠的分割掩膜，使用基于图的高效方法细化对象边界，并分别训练一个基于精子头部裁剪图像的图像网络和一个基于对应掩膜的掩膜网络。在网络中间阶段，图像特征和掩膜特征通过融合策略进行融合，以更好地学习形态特征。为了处理噪声类别标签并规范小数据集上的训练，SHMC-Net应用Soft Mixup将混合增强与损失函数相结合。我们在SCIAN和HuSHeM数据集上取得了最先进的结果，超越了使用额外预训练或昂贵集成技术的方法。