Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/16345
Title: Mammogram microcalcification cluster detection by locating key instances in a Multi-Instance Learning framework
Authors: Li, C
Lam, KM 
Zhang, L
Hui, C
Zhang, S
Keywords: Feature
Graph
Mean-shift
Microcalcification clusters
Multi-instance learning
Issue Date: 2012
Publisher: IEEE
Source: 2012 IEEE International Conference on Signal Processing, Communication and Computing (ICSPCC), 12-15 August 2012, Hong Kong, p. 175-179 How to cite?
Abstract: A new scheme for the computer-aided diagnosis (CAD) of microcalcification clusters (MCCs) detection in a Multi-Instance Learning (MIL) framework is proposed in this paper. To achieve a satisfactory performance, our algorithm first searches for possible candidates of microcalcification clusters using the mean-shift algorithm. Then, features are extracted from the potential candidates based on a constructed graph. Finally, a multi-instance learning method which locates the key instance in each bag of features is used to classify the possible candidates. Experimental results show that our scheme can achieve a superior performance on public datasets, and the computation is efficient.
URI: http://hdl.handle.net/10397/16345
ISBN: 978-1-4673-2192-1
DOI: 10.1109/ICSPCC.2012.6335723
Appears in Collections:Conference Paper

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