论文标题
根据其形态学质量对人类胚胎图像进行分类时，对深卷卷神经网络的评估
Evaluation of deep convolutional neural networks in classifying human embryo images based on their morphological quality
论文作者
论文摘要
影响体外受精（IVF）程序成功的关键因素是转移的胚胎的质量。通过手动显微镜分析进行的胚胎形态评估，由于胚胎学家的经验而导致的实践，选择标准和主观性差异。卷积神经网络（CNN）是强大的，有希望的算法，具有在许多对象类别中进行准确分类的重要潜力。网络体系结构和超参数会影响任何给定任务的CNN效率。在这里，我们评估了多层CNN从头开始开发的和流行的深度学习体系结构，例如Inception V3，Resnet，Inception-Resnet-V2，以及基于胚胎在胚胎后113小时（HPI）（HPI）的形态质量（HPI）的形态质量而在胚胎之间进行区分。 X受感受在胚胎基于其形态质量方面的区分方面表现最好。
A critical factor that influences the success of an in-vitro fertilization (IVF) procedure is the quality of the transferred embryo. Embryo morphology assessments, conventionally performed through manual microscopic analysis suffer from disparities in practice, selection criteria, and subjectivity due to the experience of the embryologist. Convolutional neural networks (CNNs) are powerful, promising algorithms with significant potential for accurate classifications across many object categories. Network architectures and hyper-parameters affect the efficiency of CNNs for any given task. Here, we evaluate multi-layered CNNs developed from scratch and popular deep-learning architectures such as Inception v3, ResNET, Inception-ResNET-v2, and Xception in differentiating between embryos based on their morphological quality at 113 hours post insemination (hpi). Xception performed the best in differentiating between the embryos based on their morphological quality.
