用注意力机制提升精子形态分类的准确率与可解释性。
Interpretable Sperm Morphology Classification via Attention-Guided Deep Learning
- 结合EfficientNet-B0与CBAM模块，聚焦精子头部关键区域。
- 在SMIDS和HuSHem数据集上准确率达90.2%与93.9%。
- 通过Grad-CAM++可视化决策依据，适合临床医生使用。
男性不育是夫妻不孕的主要原因，常与精子形态异常有关。尽管深度学习模型可实现自动化分析，但多数缺乏可解释性，限制了其临床应用。本研究提出一种基于注意力引导的深度学习框架，用于精子形态分类。将预训练的EfficientNet-B0与卷积块注意力模块（CBAM）结合，聚焦精子头部关键区域，提升准确率与可解释性。在SMIDS和HuSHem公开数据集上，模型准确率分别达到90.2%和93.9%（宏F1分数分别为0.913和0.948），优于SimpleCNN和标准EfficientNet-B0。此外，采用Grad-CAM++可视化技术揭示影响模型决策的关键特征。结果表明，该框架兼具高精度与透明性，可作为辅助生育诊所进行自动化精子分析的实用工具。
原文摘要 · Abstract (English)
Male infertility is a major cause of couple infertility, often linked to abnormal sperm morphology. While deep learning models offer automated analysis, most lack interpretability, limiting their clinical adoption. This study proposes an attention-guided deep learning framework for sperm morphology classification. We combine a pretrained EfficientNet-B0 with a Convolutional Block Attention Module (CBAM) to focus on key areas of the sperm head, improving both accuracy and interpretability. Evaluated on the SMIDS and HuSHem public datasets, our model achieves accuracies of 90.2% and 93.9% (macro F1 scores of 0.913 and 0.948), outperforming SimpleCNN and standard EfficientNet-B0. Furthermore, we use Grad-CAM++ visualizations to highlight features influencing the model's decisions. The results demonstrate that this accurate and transparent framework is a practical tool for automated sperm analysis in fertility clinics.
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