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- W4361763250 abstract "This paper proposes a graph based unsupervised learning (GBUL) clustering approach that uses features of graphs to identify different tissue structures in images, aiding histopathologists to quickly identify lesion areas and improve the accuracy of diagnosis. The first-stage rough clustering was performed by applying color features and k-means based on graph theory in Hematoxylin-eosin (H&E) stained cervical histopathology images. By applying a skeletonization based node generation (SBNG) approach, the generated nodes are approximated as a distribution of cervical cell nuclei, a minimum spanning tree (MST) is constructed from the generated nodes and their geometric features are extracted, followed by clustering by applying the k-means algorithm again. The important topological information hidden in histopathology images is applied to solve the cervical histopathology image clustering (CHIC) problem, and the function of mouse manual annotation to assist histopathologists is provided. CHIC based on MST can more accurately distinguish the tissue structure of high, medium and low density adherent cells. Combined with suspicious lesion points added manually by the physician, it can semi-automatically predict the cancer risk of tissues, rapidly identify lesion areas and improve the accuracy and efficiency of diagnosis." @default.
- W4361763250 created "2023-04-04" @default.
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- W4361763250 date "2023-01-01" @default.
- W4361763250 modified "2023-09-25" @default.
- W4361763250 title "Clustering of Cervical Histopathology Images Based on Minimum Spanning Tree" @default.
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- W4361763250 doi "https://doi.org/10.1007/978-981-99-0923-0_10" @default.
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