PDF(Pubmed)
Abstract：
Transmission electron microscopy (TEM) is an imaging technique used to visualize and analyze nano-sized structures and objects such as virus particles. Light microscopy can be used to diagnose diseases or characterize e.g. blood cells. Since samples under microscopes exhibit certain symmetries, such as global rotation invariance, equivariant neural networks are presumed to be useful. In this study, a baseline convolutional neural network is constructed in the form of the commonly used VGG16 classifier. Thereafter, it is modified to be equivariant to the p4 symmetry group of rotations of multiples of 90° using group convolutions. This yields a number of benefits on a TEM virus dataset, including higher top validation set accuracy by on average 7.6% and faster convergence during training by on average 23.1% of that of the baseline. Similarly, when training and testing on images of blood cells, the convergence time for the equivariant neural network is 7.9% of that of the baseline. From this it is concluded that augmentation strategies for rotation can be skipped. Furthermore, when modelling the accuracy versus amount of TEM virus training data with a power law, the equivariant network has a slope of - 0.43 compared to - 0.26 of the baseline. Thus the equivariant network learns faster than the baseline when more training data is added. This study extends previous research on equivariant neural networks applied to images which exhibit symmetries to isometric transformations.
摘要：
透射电子显微镜（TEM）是一种成像技术，用于可视化和分析纳米尺寸的结构和物体，例如病毒颗粒。光学显微镜可用于诊断疾病或表征例如血细胞。由于显微镜下的样品表现出某些对称性，例如全局旋转不变性，等变神经网络被认为是有用的。在这项研究中,以常用的VGG16分类器的形式构建基线卷积神经网络。此后,使用组卷积将其修改为对90°倍数旋转的p4对称组的等变量。这在TEM病毒数据集上产生了许多好处，包括最高验证集准确率平均为7.6%，训练期间收敛速度平均为基线的23.1%。同样，当训练和测试血细胞图像时，等变神经网络的收敛时间是基线的7.9%。由此得出结论，可以跳过旋转的增强策略。此外，当用幂律对TEM病毒训练数据的准确性与数量进行建模时，与基线的-0.26相比，等变网络的斜率为-0.43。因此，当添加更多的训练数据时，等变网络比基线更快地学习。这项研究扩展了先前对等变神经网络的研究，该等变神经网络应用于表现出对称性的图像等距变换。
