Larin Ilya, Karabelsky Alexander
Center for Translational Medicine, Sirius University of Science and Technology, Federal Territory Sirius, 1 Olympic Ave., Sirius 354340, Russia.
J Imaging. 2025 Mar 29;11(4):103. doi: 10.3390/jimaging11040103.
The development of neural networks has made the introduction of multimodal systems inevitable. Computer vision methods are still not widely used in biological research, despite their importance. It is time to recognize the significance of advances in feature extraction and real-time analysis of information from cells. Teacherless learning for the image clustering task is of great interest. In particular, the clustering of single cells is of great interest. This study will evaluate the feasibility of using latent representation and clustering of single cells in various applications in the fields of medicine and biotechnology. Of particular interest are embeddings, which relate to the morphological characterization of cells. Studies of C2C12 cells will reveal more about aspects of muscle differentiation by using neural networks. This work focuses on analyzing the applicability of the latent space to extract morphological features. Like many researchers in this field, we note that obtaining high-quality latent representations for phase-contrast or bright-field images opens new frontiers for creating large visual-language models. Graph structures are the main approaches to non-Euclidean manifolds. Graph-based segmentation has a long history, e.g., the normalized cuts algorithm treated segmentation as a graph partitioning problem-but only recently have such ideas merged with deep learning in an unsupervised manner. Recently, a number of works have shown the advantages of hyperbolic embeddings in vision tasks, including clustering and classification based on the Poincaré ball model. One area worth highlighting is unsupervised segmentation, which we believe is undervalued, particularly in the context of non-Euclidean spaces. In this approach, we aim to mark the beginning of our future work on integrating visual information and biological aspects of individual cells to multimodal space in comparative studies in vitro.
神经网络的发展使得多模态系统的引入成为必然。尽管计算机视觉方法很重要，但在生物学研究中仍未得到广泛应用。现在是时候认识到细胞特征提取和信息实时分析进展的重要性了。无监督学习用于图像聚类任务备受关注。特别是单细胞聚类非常有趣。本研究将评估在医学和生物技术领域的各种应用中使用单细胞的潜在表示和聚类的可行性。特别令人感兴趣的是与细胞形态特征相关的嵌入。通过使用神经网络对C2C12细胞的研究将揭示更多关于肌肉分化的方面。这项工作专注于分析潜在空间在提取形态特征方面的适用性。和该领域的许多研究人员一样，我们注意到为相差或明场图像获得高质量的潜在表示为创建大型视觉语言模型开辟了新的前沿领域。图结构是处理非欧几里得流形的主要方法。基于图的分割有着悠久的历史，例如归一化割算法将分割视为图划分问题，但直到最近这些想法才以无监督的方式与深度学习相结合。最近，许多工作已经展示了双曲嵌入在视觉任务中的优势，包括基于庞加莱球模型的聚类和分类。一个值得强调的领域是无监督分割，我们认为它被低估了，特别是在非欧几里得空间的背景下。在这种方法中，我们旨在开启我们未来的工作，即在体外比较研究中将单个细胞的视觉信息和生物学方面整合到多模态空间中。
