Mammogram microcalcification cluster detection by locating key instances in a Multi-Instance Learning framework

Chao Li; Kin Man Lam; Lei Zhang; Chun Hui; Su Zhang

doi:10.1109/ICSPCC.2012.6335723

Mammogram microcalcification cluster detection by locating key instances in a Multi-Instance Learning framework

Chao Li, Kin Man Lam, Lei Zhang, Chun Hui, Su Zhang

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

8 Citations (Scopus)

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.

Original language	English
Title of host publication	2012 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2012
Pages	175-179
Number of pages	5
DOIs	https://doi.org/10.1109/ICSPCC.2012.6335723
Publication status	Published - 26 Nov 2012
Event	2012 2nd IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2012 - Hong Kong, Hong Kong Duration: 12 Aug 2012 → 15 Aug 2012

Conference

Conference	2012 2nd IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2012
Country/Territory	Hong Kong
City	Hong Kong
Period	12/08/12 → 15/08/12

Keywords

feature
graph
mean-shift
microcalcification clusters
multi-instance learning

ASJC Scopus subject areas

Computer Networks and Communications
Signal Processing

More information

10.1109/ICSPCC.2012.6335723

Cite this

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title = "Mammogram microcalcification cluster detection by locating key instances in a Multi-Instance Learning framework",

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.",

keywords = "feature, graph, mean-shift, microcalcification clusters, multi-instance learning",

author = "Chao Li and Lam, {Kin Man} and Lei Zhang and Chun Hui and Su Zhang",

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doi = "10.1109/ICSPCC.2012.6335723",

language = "English",

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Li, C, Lam, KM , Zhang, L, Hui, C & Zhang, S 2012, Mammogram microcalcification cluster detection by locating key instances in a Multi-Instance Learning framework. in 2012 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2012., 6335723, pp. 175-179, 2012 2nd IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2012, Hong Kong, Hong Kong, 12/08/12. https://doi.org/10.1109/ICSPCC.2012.6335723

Mammogram microcalcification cluster detection by locating key instances in a Multi-Instance Learning framework. / Li, Chao; Lam, Kin Man ; Zhang, Lei et al.
2012 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2012. 2012. p. 175-179 6335723.

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

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PY - 2012/11/26

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AB - 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.

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KW - graph

KW - mean-shift

KW - microcalcification clusters

KW - multi-instance learning

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BT - 2012 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2012

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Mammogram microcalcification cluster detection by locating key instances in a Multi-Instance Learning framework

Abstract

Conference

Keywords

ASJC Scopus subject areas

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