H. Deng

J. Amores

J.R.R. Uijlings

M. Kandemir

Y. Chen

Y. Xu

Z. Fu

English

Wenqi Li

Jianguo Zhang

Stephen J. McKenna

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Multiple Instance Cancer Detection by Boosting Regularised Trees

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

Li, Wenqi

Zhang, Jianguo

McKenna, Stephen

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Multiple instance cancer detection by boosting regularised trees

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http://discovery.dundee.ac.uk/portal/files/7428613/opt_miccai15_wenqi_uopdated_DOI.pdf

Multiple instance cancer detection by boosting regularised trees

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