GM-Citation-KNN: graph matching based multiple instance learning algorithm

摘要

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.