Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/107547
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Type: | Conference paper |
Title: | Tree RE-weighted belief propagation using deep learning potentials for mass segmentation from mammograms |
Author: | Dhungel, N. Carneiro, G. Bradley, A. |
Citation: | Proceedings / IEEE International Symposium on Biomedical Imaging: from nano to macro. IEEE International Symposium on Biomedical Imaging, 2015, vol.2015-July, pp.760-763 |
Publisher: | IEEE |
Issue Date: | 2015 |
Series/Report no.: | IEEE International Symposium on Biomedical Imaging |
ISBN: | 9781479923748 |
ISSN: | 1945-7928 1945-8452 |
Conference Name: | 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI 2015) (16 Apr 2015 - 19 Apr 2015 : New York, NY) |
Statement of Responsibility: | Neeraj Dhungely, Gustavo Carneiroy, Andrew P. Bradley |
Abstract: | In this paper, we propose a new method for the segmentation of breast masses from mammograms using a conditional random field (CRF) model that combines several types of potential functions, including one that classifies image regions using deep learning. The inference method used in this model is the tree re-weighted (TRW) belief propagation, which allows a learning mechanism that directly minimizes the mass segmentation error and an inference approach that produces an optimal result under the approximations of the TRW formulation. We show that the use of these inference and learning mechanisms and the deep learning potential functions provides gains in terms of accuracy and efficiency in comparison with the current state of the art using the publicly available datasets INbreast and DDSM-BCRP. |
Keywords: | Mammograms, mass segmentation, tree re-weighted belief propagation, deep learning, Gaussian mixture model |
Rights: | © 2015 Crown |
DOI: | 10.1109/ISBI.2015.7163983 |
Grant ID: | http://purl.org/au-research/grants/arc/DP140102794 http://purl.org/au-research/grants/arc/FT110100623 |
Published version: | http://dx.doi.org/10.1109/isbi.2015.7163983 |
Appears in Collections: | Aurora harvest 8 Computer Science publications |
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RA_hdl_107547.pdf Restricted Access | Restricted Access | 1.42 MB | Adobe PDF | View/Open |
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