29 August 2016 GM-Citation-KNN: Graph matching based multiple instance learning algorithm
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Proceedings Volume 10033, Eighth International Conference on Digital Image Processing (ICDIP 2016); 100334J (2016); doi: 10.1117/12.2243632
Event: Eighth International Conference on Digital Image Processing (ICDIP 2016), 2016, Chengu, China
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.
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Chao Li, Chuqing Cao, Yunfeng Gao, "GM-Citation-KNN: Graph matching based multiple instance learning algorithm", Proc. SPIE 10033, Eighth International Conference on Digital Image Processing (ICDIP 2016), 100334J (29 August 2016); doi: 10.1117/12.2243632; http://dx.doi.org/10.1117/12.2243632
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KEYWORDS
Genetic algorithms

Optimization (mathematics)

Breast

Feature extraction

Mammography

Databases

Image classification

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