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.
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Acknowledgements
We thank the funds supported by the “National Natural Science Foundation of China” (No. 61806047), the “Fundamental Research Funds for the Central Universities” (No. N171903004), and the “Scientific Research Launched Fund of Liaoning Shihua University” (No. 2017XJJ-061). We also thank Zhijie Hu, due to his contribution is considered as important as the first author in this paper.
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Li, C. et al. (2020). Cervical Histopathology Image Clustering Using Graph Based Unsupervised Learning. In: Wang, R., Chen, Z., Zhang, W., Zhu, Q. (eds) Proceedings of the 11th International Conference on Modelling, Identification and Control (ICMIC2019). Lecture Notes in Electrical Engineering, vol 582. Springer, Singapore. https://doi.org/10.1007/978-981-15-0474-7_14
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DOI: https://doi.org/10.1007/978-981-15-0474-7_14
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