Riemannian Manifolds for Biological Imaging Applications Based on Unsupervised Learning
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 info...
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MDPI AG
2025-03-01
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| author | Ilya Larin Alexander Karabelsky |
| author_facet | Ilya Larin Alexander Karabelsky |
| author_sort | Ilya Larin |
| collection | DOAJ |
| description | 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. |
| format | Article |
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| institution | DOAJ |
| issn | 2313-433X |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
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| series | Journal of Imaging |
| spelling | doaj-art-6dc6c49fa23b48628af3ad4d7f5ac8fa2025-08-20T03:13:45ZengMDPI AGJournal of Imaging2313-433X2025-03-0111410310.3390/jimaging11040103Riemannian Manifolds for Biological Imaging Applications Based on Unsupervised LearningIlya Larin0Alexander Karabelsky1Center for Translational Medicine, Sirius University of Science and Technology, Federal Territory Sirius, 1 Olympic Ave., Sirius 354340, RussiaCenter for Translational Medicine, Sirius University of Science and Technology, Federal Territory Sirius, 1 Olympic Ave., Sirius 354340, RussiaThe 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.https://www.mdpi.com/2313-433X/11/4/103C2C12t-SNE representationRiemannian manifold |
| spellingShingle | Ilya Larin Alexander Karabelsky Riemannian Manifolds for Biological Imaging Applications Based on Unsupervised Learning Journal of Imaging C2C12 t-SNE representation Riemannian manifold |
| title | Riemannian Manifolds for Biological Imaging Applications Based on Unsupervised Learning |
| title_full | Riemannian Manifolds for Biological Imaging Applications Based on Unsupervised Learning |
| title_fullStr | Riemannian Manifolds for Biological Imaging Applications Based on Unsupervised Learning |
| title_full_unstemmed | Riemannian Manifolds for Biological Imaging Applications Based on Unsupervised Learning |
| title_short | Riemannian Manifolds for Biological Imaging Applications Based on Unsupervised Learning |
| title_sort | riemannian manifolds for biological imaging applications based on unsupervised learning |
| topic | C2C12 t-SNE representation Riemannian manifold |
| url | https://www.mdpi.com/2313-433X/11/4/103 |
| work_keys_str_mv | AT ilyalarin riemannianmanifoldsforbiologicalimagingapplicationsbasedonunsupervisedlearning AT alexanderkarabelsky riemannianmanifoldsforbiologicalimagingapplicationsbasedonunsupervisedlearning |