College of Information Science and Technology, Donghua University, Shanghai, 201620, China.
Nanjing University of Chinese Medicine Hanlin College, Taizhou, Jiangsu, China.
Med Biol Eng Comput. 2019 Jun;57(6):1187-1198. doi: 10.1007/s11517-018-01946-z. Epub 2019 Jan 28.
The development of computer technology now allows the quick and efficient automatic fluorescence microscopy generation of a large number of images of proteins in specific subcellular compartments using fluorescence microscopy. Digital image processing and pattern recognition technology can easily classify these images, identify the subcellular location of proteins, and subsequently carry out related work such as analysis and investigation of protein function. Here, based on a fluorescence microscopy 2D image dataset of HeLa cells, the CapsNet network model was used to classify ten types of images of proteins in different subcellular compartments. Capsules in the CapsNet network model were trained to capture the possibility of certain features and variants rather than to capture the characteristics of a specific variant. The capsule at the same level predicted the instantiation parameters of the higher level capsule through the transformation matrix, and the higher level capsule became active when multiple dynamic routing forecasts were consistent. Experiments show that using the CapsNet network model to classify 2D HeLa datasets can achieve higher accuracy. Graphical abstract ᅟ.
计算机技术的发展使得利用荧光显微镜快速有效地自动生成大量特定亚细胞区室中蛋白质的荧光显微镜图像成为可能。数字图像处理和模式识别技术可以轻松对这些图像进行分类，识别蛋白质的亚细胞位置，并随后进行相关工作，如蛋白质功能的分析和研究。在这里，基于 HeLa 细胞的荧光显微镜 2D 图像数据集，使用 CapsNet 网络模型对不同亚细胞区室中十种类型的蛋白质图像进行分类。CapsNet 网络模型中的胶囊被训练来捕获某些特征和变体的可能性，而不是捕获特定变体的特征。同一级别的胶囊通过变换矩阵预测更高一级胶囊的实例化参数，当多个动态路由预测一致时，更高一级胶囊就会变得活跃。实验表明，使用 CapsNet 网络模型对 2D HeLa 数据集进行分类可以获得更高的准确性。图摘要。
