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Proceedings Paper

GM-Citation-KNN: Graph matching based multiple instance learning algorithm
Author(s): Chao Li; Chuqing Cao; Yunfeng Gao
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Paper Abstract

Multiple instance learning algorithms have been increasingly utilized in many applications. In this paper, we propose a novel multiple instance learning method called GM-Citation-KNN for the microcalcification clusters (MCCs) detection and classification in breast images. After image preprocessing and candidates generation, features are extracted from the potential candidates based on a constructed graph. Then an improved version of Citation-KNN algorithm is used for classification. Regarding each bag as a graph, GM-Citation-KNN calculate the graph similarity to replace the Hausdoff distance in Citation-KNN. The graph similarity is computed by many-to-many graph matching which allows the comparison of parts between graphs. The proposed algorithms were validated on the public breast dataset. Experimental results show that our algorithm can achieve a superior performance compared with some state-of-art MIL algorithms.

Paper Details

Date Published: 29 August 2016
PDF: 5 pages
Proc. SPIE 10033, Eighth International Conference on Digital Image Processing (ICDIP 2016), 100334J (29 August 2016); doi: 10.1117/12.2243632
Show Author Affiliations
Chao Li, HIT Wuhu Robot Technology Research Institute (China)
Chuqing Cao, HIT Wuhu Robot Technology Research Institute (China)
Yunfeng Gao, HIT Wuhu Robot Technology Research Institute (China)
Harbin Institute of Technology (China)


Published in SPIE Proceedings Vol. 10033:
Eighth International Conference on Digital Image Processing (ICDIP 2016)
Charles M. Falco; Xudong Jiang, Editor(s)

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