Multiple instance learning for breast cancer magnetic resonance imaging

Maken, Fahira A., Gal, Yaniv, McClymont, Darryl and Bradley, Andrew P. (2014). Multiple instance learning for breast cancer magnetic resonance imaging. In: Abdesselam Bouzerdoum, Lei Wang, Philip Ogunbona, Wanqing Li and Son Lam Phung, 2014 International Conference on Digital Image Computing: Techniques and Applications (DICTA). Digital Image Computing: Techniques and Applications (DICTA), Wollongong, NSW, Australia, (). 25-27 November 2014. doi:10.1109/DICTA.2014.7008118

Attached Files (Some files may be inaccessible until you login with your UQ eSpace credentials)
Name Description MIMEType Size Downloads

Author Maken, Fahira A.
Gal, Yaniv
McClymont, Darryl
Bradley, Andrew P.
Title of paper Multiple instance learning for breast cancer magnetic resonance imaging
Conference name Digital Image Computing: Techniques and Applications (DICTA)
Conference location Wollongong, NSW, Australia
Conference dates 25-27 November 2014
Proceedings title 2014 International Conference on Digital Image Computing: Techniques and Applications (DICTA)
Place of Publication Piscataway, NJ, United States
Publisher IEEE
Publication Year 2014
Sub-type Fully published paper
DOI 10.1109/DICTA.2014.7008118
Open Access Status
ISBN 9781479954094
Editor Abdesselam Bouzerdoum
Lei Wang
Philip Ogunbona
Wanqing Li
Son Lam Phung
Total pages 8
Collection year 2015
Language eng
Abstract/Summary In this paper we evaluate the suitability of multiple instance learning (MIL) for the classification of T2 weighted magnetic resonance images (MRI) of the breast. Specifically, we compare the performance of citation-kNN against traditional kNN and a random forest (RF) classifier. We utilise both (generic) tile-based features and (domain specific) region-of-interest (ROI) based features We perform experiments on two datasets consisting of A) mass-like lesions and B) both mass-like and non-mass-like lesions. The performance of citation-kNN as both a diagnostic and screening tool is evaluated using the area under the receiver operating characteristics curve (AUC), estimated over 10-fold cross-validation. Results demonstrate that citation- kNN has equivalent performance to traditional kNN and RF. However, the tile-based approach used by citation-kNN does not require the domain specific ROI-based features typically used in breast MRI. This not only makes citation-kNN robust to inaccuracies in the delineation of suspicious lesions, but also makes it suitable for use as a screening tool, where the aim is to discriminate lesions from normal tissue.
Q-Index Code E1
Q-Index Status Confirmed Code
Institutional Status UQ

 
Versions
Version Filter Type
Citation counts: Scopus Citation Count Cited 0 times in Scopus Article
Google Scholar Search Google Scholar
Created: Sat, 07 Mar 2015, 16:12:57 EST by Andrew Bradley on behalf of School of Information Technol and Elec Engineering