Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/107544
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Type: Conference paper
Title: The automated learning of deep features for breast mass classification from mammograms
Author: Dhungel, N.
Carneiro, G.
Bradley, A.
Citation: Proceedings of the 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, Part II - MICCAI 2016, 2016 / vol.9901, pp.106-114
Publisher: Springer
Issue Date: 2016
Series/Report no.: Lecture Notes in Computer Science
ISBN: 9783319467221
ISSN: 0302-9743
1611-3349
Conference Name: 19th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI 2016) (17 Oct 2016 - 21 Oct 2016 : Athens, Greece)
Statement of
Responsibility: 
Neeraj Dhungel, Gustavo Carneiro, and Andrew P. Bradley
Abstract: The classification of breast masses from mammograms into benign or malignant has been commonly addressed with machine learning classifiers that use as input a large set of hand-crafted features, usually based on general geometrical and texture information. In this paper, we propose a novel deep learning method that automatically learns features based directly on the optmisation of breast mass classification from mammograms, where we target an improved classification performance compared to the approach described above. The novelty of our approach lies in the two-step training process that involves a pre-training based on the learning of a regressor that estimates the values of a large set of handcrafted features, followed by a fine-tuning stage that learns the breast mass classifier. Using the publicly available INbreast dataset, we show that the proposed method produces better classification results, compared with the machine learning model using hand-crafted features and with deep learning method trained directly for the classification stage without the pre-training stage. We also show that the proposed method produces the current state-of-the-art breast mass classification results for the INbreast dataset. Finally, we integrate the proposed classifier into a fully automated breast mass detection and segmentation, which shows promising results.
Keywords: Deep learning, breast mass classification, mammograms
Rights: © Springer International Publishing AG 2016
RMID: 0030059195
DOI: 10.1007/978-3-319-46723-8_13
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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