Department of Mechanical and Industrial Engineering, University of Toronto, 5 King's College Road, Toronto, ON, M5S 3G8, Canada.
Comput Biol Med. 2019 Aug;111:103342. doi: 10.1016/j.compbiomed.2019.103342. Epub 2019 Jun 25.
Infertility is a global health concern, and couples are increasingly seeking medical assistance to achieve reproduction. Semen analysis is a primary assessment performed by a clinician, in which the morphology of the sperm population is evaluated. Machine learning algorithms that automate, standardize, and expedite sperm classification are the subject of ongoing research.
We demonstrate a deep learning method to classify sperm into one of several World Health Organization (WHO) shape-based categories. Our method uses VGG16, a deep convolutional neural network (CNN) initially trained on ImageNet, a collection of human-annotated everyday images, which we retrain for sperm classification using two freely-available sperm head datasets (HuSHeM and SCIAN).
Our deep learning approach classifies sperm at high accuracy and performs well in head-to-head comparisons with earlier approaches using identical datasets. We demonstrate improvement in true positive rate over a classifier approach based on a cascade ensemble of support vector machines (CE-SVM) and show similar true positive rates as compared to an adaptive patch-based dictionary learning (APDL) method. Retraining an off-the-shelf VGG16 network avoids excessive neural network computation or having to learn and use the massive dictionaries required for sparse representation, both of which can be computationally expensive.
We show that our deep learning approach to sperm head classification represents a viable method to automate, standardize, and accelerate semen analysis. Our approach highlights the potential of artificial intelligence technologies to eventually exceed human experts in terms of accuracy, reliability, and throughput.
不孕不育是一个全球性的健康问题，越来越多的夫妇寻求医疗帮助来实现生育。精液分析是临床医生进行的主要评估，其中评估精子群体的形态。自动化、标准化和加速精子分类的机器学习算法是正在研究的主题。
我们展示了一种深度学习方法，可将精子分为世界卫生组织（WHO）基于形状的几个类别之一。我们的方法使用 VGG16，这是一种最初在 ImageNet 上训练的深度卷积神经网络（CNN），ImageNet 是一个包含人类注释的日常图像集合，我们使用两个免费的精子头数据集（HuSHeM 和 SCIAN）重新训练 VGG16 进行精子分类。
我们的深度学习方法可以高精度地对精子进行分类，并在与使用相同数据集的早期方法进行的头对头比较中表现良好。我们证明了在基于级联集成支持向量机（CE-SVM）的分类器方法上的真阳性率有所提高，并显示出与自适应基于补丁的字典学习（APDL）方法相似的真阳性率。重新训练现成的 VGG16 网络避免了过多的神经网络计算，也避免了必须学习和使用稀疏表示所需的大规模字典，这两者都可能计算成本很高。
我们表明，我们的精子头分类深度学习方法代表了一种可行的方法，可以实现自动化、标准化和加速精液分析。我们的方法强调了人工智能技术最终在准确性、可靠性和吞吐量方面超越人类专家的潜力。
