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- W2991685053 abstract "In order to apply the important topological information to solve a Cervical Histopathology Image Clustering (CHIC) problem, a Graph Based Unsupervised Learning (GBUL) approach is proposed in this paper. First, the GBUL method applies color features and k-means clustering for a first-stage “coarse” clustering. Then, a Skeletonization Based Node Generation (SBNG) approach is introduced to approximate the distribution of cervical cell nuclei. Thirdly, based on the SBNG nodes, a minimum spanning tree graph is constructed. Next, graph features and additional geometrical features are extracted based on the constructed graph. Finally, the k-means clustering is applied again for the second-stage clustering. In the experiment, a practical cervical histopathology image dataset with ten whole scanned images is tested, obtaining a promising CHIC result and showing a huge potential in the cancer risk prediction field." @default.
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- W2991685053 date "2019-12-04" @default.
- W2991685053 modified "2023-10-15" @default.
- W2991685053 title "Cervical Histopathology Image Clustering Using Graph Based Unsupervised Learning" @default.
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- W2991685053 doi "https://doi.org/10.1007/978-981-15-0474-7_14" @default.
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