Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/107613
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Type: Conference paper
Title: Deep learning and structured prediction for the segmentation of mass in mammograms
Author: Dhungel, N.
Carneiro, G.
Bradley, A.
Citation: Proceedings of the 18th International Conference on Medical Image Computing and Computer-Assisted Intervention - Part 1, MICCAI 2015,, 2015 / Navab, N., Hornegger, J., Wells, W., Frangi, A. (ed./s), vol.9349, pp.605-612
Publisher: Springer
Issue Date: 2015
Series/Report no.: Lecture Notes in Computer Science
ISBN: 978-3-319-24552-2
ISSN: 0302-9743
1611-3349
Conference Name: 18th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI 2015) (05 Oct 2015 - 09 Oct 2015 : Munich, GERMANY)
Statement of
Responsibility: 
Neeraj Dhungel, Gustavo Carneiro, and Andrew P. Bradley
Abstract: In this paper, we explore the use of deep convolution and deep belief networks as potential functions in structured prediction models for the segmentation of breast masses from mammograms. In particular, the structured prediction models are estimated with loss minimization parameter learning algorithms, representing: a) conditional random field (CRF), and b) structured support vector machine (SSVM). For the CRF model, we use the inference algorithm based on tree re-weighted belief propagation with truncated fitting training, and for the SSVM model the inference is based on graph cuts with maximum margin training. We show empirically the importance of deep learning methods in producing state-of-the-art results for both structured prediction models. In addition, we show that our methods produce results that can be considered the best results to date on DDSM-BCRP and INbreast databases. Finally, we show that the CRF model is significantly faster than SSVM, both in terms of inference and training time, which suggests an advantage of CRF models when combined with deep learning potential functions.
Keywords: Deep learning, structured output learning, mammogram segmentation
Rights: © Springer International Publishing Switzerland 2015
RMID: 0030039962
DOI: 10.1007/978-3-319-24553-9_74
Grant ID: http://purl.org/au-research/grants/arc/DP140102794
http://purl.org/au-research/grants/arc/FT110100623
Appears in Collections:Computer Science publications

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