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Multiple instance cancer detection by boosting regularised trees

Multiple instance cancer detection by boosting regularised trees

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Original languageEnglish
Title of host publicationMedical Image Computing and Computer-Assisted Intervention (MICCAI 2015)
Subtitle of host publication18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part 1
EditorsNassir Navab, Joachim Hornegger, William M. Wells, Alejandro F. Frangi
PublisherSpringer
Pages645-652
Number of pages8
Volume9349
ISBN (Electronic)9783319245539
ISBN (Print)9783319245522
DOIs
StatePublished - 2015
EventMedical Image Computing and Computer Assisted Interventions18th International Conference - Munich, Germany

Publication series

NameLecture notes in computer science
PublisherSpringer
Volume9349

Conference

ConferenceMedical Image Computing and Computer Assisted Interventions18th International Conference
CountryGermany
CityMunich
Period5/10/159/10/15

Abstract

We propose a novel multiple instance learning algorithm for cancer detection in histopathology images. With images labelled at image-level, we rst search a set of region-level prototypes by solving a submodular set cover problem. Regularised regression trees are then constructed and combined on the set of prototypes using a multiple instance boosting framework. The method compared favourably with competing methods in experiments on breast cancer tissue microarray images and optical tomographic images of colorectal polyps.

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    Submitted manuscript, 1 MB, PDF-document

    The final publication is available at Springer via http://10.1007/978-3-319-24553-9_79

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