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 proceedingConference article published in proceeding or bookAcademic researchpeer-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 languageEnglish
Title of host publication2012 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2012
Pages175-179
Number of pages5
DOIs
Publication statusPublished - 26 Nov 2012
Event2012 2nd IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2012 - Hong Kong, Hong Kong
Duration: 12 Aug 201215 Aug 2012

Conference

Conference2012 2nd IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2012
Country/TerritoryHong Kong
CityHong Kong
Period12/08/1215/08/12

Keywords

  • feature
  • graph
  • mean-shift
  • microcalcification clusters
  • multi-instance learning

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Signal Processing

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