Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/107614
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Type: Conference paper
Title: Unregistered multiview mammogram analysis with pre-trained deep learning models
Author: Carneiro, G.
Nascimento, J.
Bradley, A.
Citation: Proceedings of the 18th International Conference on Medical Image Computing and Computer-Assisted Intervention, Part III - MICCAI 2015, 2015 / Navab, N., Hornegger, J., Wells, W., Frangi, A. (ed./s), vol.9351, pp.652-660
Publisher: Springer
Issue Date: 2015
Series/Report no.: Lecture Notes in Computer Science
ISBN: 9783319245737
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: 
Gustavo Carneiro, Jacinto Nascimento, and Andrew P. Bradley
Abstract: We show two important findings on the use of deep convolutional neural networks (CNN) in medical image analysis. First, we show that CNN models that are pre-trained using computer vision databases (e.g., Imagenet) are useful in medical image applications, despite the significant differences in image appearance. Second, we show that multiview classification is possible without the pre-registration of the input images. Rather, we use the high-level features produced by the CNNs trained in each view separately. Focusing on the classification of mammograms using craniocaudal (CC) and mediolateral oblique (MLO) views and their respective mass and micro-calcification segmentations of the same breast, we initially train a separate CNN model for each view and each segmentation map using an Imagenet pre-trained model. Then, using the features learned from each segmentation map and unregistered views, we train a final CNN classifier that estimates the patient's risk of developing breast cancer using the Breast Imaging-Reporting and Data System (BI-RADS) score. We test our methodology in two publicly available datasets (InBreast and DDSM), containing hundreds of cases, and show that it produces a volume under ROC surface of over 0.9 and an area under ROC curve (for a 2-class problem - benign and malignant) of over 0.9. In general, our approach shows state-of-the-art classification results and demonstrates a new comprehensive way of addressing this challenging classification problem.
Keywords: Deep learning, mammogram, multiview classification
Rights: © Springer International Publishing Switzerland 2015
RMID: 0030041173
DOI: 10.1007/978-3-319-24574-4_78
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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