Multiple instance cancer detection by boosting regularised trees

Research output: Chapter in Book/Report/Conference proceedingChapter (peer-reviewed)

13 Citations (Scopus)
399 Downloads (Pure)

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.
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
Publication statusPublished - 2015
EventMedical Image Computing and Computer Assisted Interventions18th International Conference - Munich, Germany
Duration: 5 Oct 20159 Oct 2015

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

Fingerprint Dive into the research topics of 'Multiple instance cancer detection by boosting regularised trees'. Together they form a unique fingerprint.

  • Student Theses

    Analysis of Colorectal Polyps in Optical Projection Tomography

    Author: Li, W., 2015

    Supervisor: Zhang, J. (Supervisor) & McKenna, S. (Supervisor)

    Student thesis: Doctoral ThesisDoctor of Philosophy

    File

    Cite this

    Li, W., Zhang, J., & McKenna, S. (2015). Multiple instance cancer detection by boosting regularised trees. In N. Navab, J. Hornegger, W. M. Wells, & A. F. Frangi (Eds.), Medical Image Computing and Computer-Assisted Intervention (MICCAI 2015): 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part 1 (Vol. 9349, pp. 645-652). (Lecture notes in computer science ; Vol. 9349). Springer . https://doi.org/10.1007/978-3-319-24553-9_79