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Leveraging graph Theory for Advanced Embryo Stage Identification in Assisted Reproductive Technologies 47th

Leveraging Graph Theory for Advanced Embryo Stage Identifica...

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47th Mexican Conference on Biomedical Engineering

作者： Munoz-Herrera, Saul Paredes, Omar Univ Guadalajara Biodigital Innovat Lab CUCEI Guadalajara Mexico IVF 2 0 Ltd London England

ISBN: (纸本)9783031821226;9783031821233

In recent years, the use of graph theory in image analysis has gained traction, offering a flexible approach to handling complex data. This study explores the application of graph-based clustering techniques to embryo images captured during various developmental stages. We represent these images as graphs, where nodes correspond to enriched features extracted from image patches, and edges are established based on proximity and visual similarities. Dimensionality reduction was performed using Principal Component Analysis (PCA) and t-Distributed Stochastic Neighbor Embedding (t-SNE), followed by clustering using algorithms such as KMeans, Agglomerative Clustering, Gaussian Mixture Models (GMM), Spectral Clustering, and Birch. Our results indicate that GMM outperformed other methods, achieving the highest scores in metrics such as Adjusted Rand Index (0.1673), Normalized Mutual Information (0.2455), Homogeneity (0.2443), Completeness (0.2467), and V-Measure (0.2455), along with a strong Silhouette Score (0.4026). Notably, significant clustering tendencies were observed in the tB and tPN stages, while other stages exhibited a more mixed distribution, particularly in the tn and tSC stages. This research not only pioneers the use of graph-based methods in embryology but also suggests the potential for future improvements through the integration of advanced deep learning techniques and the expansion of data sets. The findings contribute significantly to the field of reproductive medicine, providing new tools for the analysis and classification of embryonic development stages.

关键词： graph-based data representation Embryo Image Analysis Image-based Network Generation Clustering Algorithms

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A Unifying Model for Representing Time-Varying graphs

A Unifying Model for Representing Time-Varying Graphs

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Proceedings of the 2015 IEEE International Conference on data Science and Advanced Analytics (IEEE DSAA 2015)

作者： Wehmuth, Klaus Ziviani, Artur Fleury, Eric Natl Lab Sci Comp LNCC Dept Comp Sci Av Getulio Vargas 333 BR-25651075 Petropolis RJ Brazil UCB Lyon 1 ENS Lyon INRIA Univ Lyon UMR CNRSENS LyonINRIA 5668 F-69364 Lyon 07 France

ISBN: (纸本)9781467382731

graph-based models form a fundamental aspect of data representation in data Sciences and play a key role in modeling complex networked systems. In particular, recently there is an ever-increasing interest in modeling dynamic complex networks, i.e. networks in which the topological structure (nodes and edges) may vary over time. In this context, we propose a novel model for representing finite discrete Time-Varying graphs (TVGs), which are typically used to model dynamic complex networked systems. We analyze the data structures built from our proposed model and demonstrate that, for most practical cases, the asymptotic memory complexity of our model is in the order of the cardinality of the set of edges. Further, we show that our proposal is an unifying model that can represent several previous (classes of) models for dynamic networks found in the recent literature, which in general are unable to represent each other. In contrast to previous models, our proposal is also able to intrinsically model cyclic (i.e. periodic) behavior in dynamic networks. These representation capabilities attest the expressive power of our proposed unifying model for TVGs. We thus believe our unifying model for TVGs is a step forward in the theoretical foundations for data analysis of complex networked systems.

关键词： graph-based data representation dynamic network temporal network complex networked system

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