University of California-Riverside, Center for Research in Intelligent Systems, Riverside, Californi, United States.
University of California-Riverside, Stem Cell Center, Riverside, California, United States.
J Biomed Opt. 2021 Apr;26(5). doi: 10.1117/1.JBO.26.5.052913.
Automated understanding of human embryonic stem cell (hESC) videos is essential for the quantified analysis and classification of various states of hESCs and their health for diverse applications in regenerative medicine.
This paper aims to develop an ensemble method and bagging of deep learning classifiers as a model for hESC classification on a video dataset collected using a phase contrast microscope.
The paper describes a deep learning-based random network (RandNet) with an autoencoded feature extractor for the classification of hESCs into six different classes, namely, (1) cell clusters, (2) debris, (3) unattached cells, (4) attached cells, (5) dynamically blebbing cells, and (6) apoptotically blebbing cells. The approach uses unlabeled data to pre-train the autoencoder network and fine-tunes it using the available annotated data.
The proposed approach achieves a classification accuracy of 97.23 ± 0.94 % and outperforms the state-of-the-art methods. Additionally, the approach has a very low training cost compared with the other deep-learning-based approaches, and it can be used as a tool for annotating new videos, saving enormous hours of manual labor.
RandNet is an efficient and effective method that uses a combination of subnetworks trained using both labeled and unlabeled data to classify hESC images.
自动理解人类胚胎干细胞 (hESC) 视频对于量化分析和分类 hESC 的各种状态及其在再生医学中的各种应用的健康状况至关重要。
本文旨在开发一种集成方法和深度学习分类器的装袋作为使用相差显微镜收集的视频数据集上 hESC 分类的模型。
本文描述了一种基于深度学习的随机网络 (RandNet)，具有自动编码特征提取器，用于将 hESC 分为六个不同的类别，即 (1) 细胞簇、(2) 碎片、(3) 未附着细胞、(4) 附着细胞、(5) 动态起泡细胞和 (6) 凋亡性起泡细胞。该方法使用未标记的数据对自动编码器网络进行预训练，并使用可用的注释数据对其进行微调。
所提出的方法实现了 97.23 ± 0.94% 的分类精度，优于最先进的方法。此外，与其他基于深度学习的方法相比，该方法的训练成本非常低，可用于注释新视频，节省大量人工劳动。
RandNet 是一种高效有效的方法，它使用结合使用标记和未标记数据训练的子网组合来对 hESC 图像进行分类。
