Fully automated classification of mammograms using deep residual neural networks
Date
2017
Authors
Dhungel, N.
Carneiro, G.
Bradley, A.
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Conference paper
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Proceedings / IEEE International Symposium on Biomedical Imaging: from nano to macro. IEEE International Symposium on Biomedical Imaging, 2017, pp.310-314
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Neeraj Dhungel, Gustavo Carneiro, Andrew P. Bradley
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IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017) (18 Apr 2017 - 21 Apr 2017 : Melbourne, AUSTRALIA)
Abstract
In this paper, we propose a multi-view deep residual neural network (mResNet) for the fully automated classification of mammograms as either malignant or normal/benign. Specifically, our mResNet approach consists of an ensemble of deep residual networks (ResNet), which have six input images, including the unregistered craniocaudal (CC) and mediolateral oblique (MLO) mammogram views as well as the automatically produced binary segmentation maps of the masses and micro-calcifications in each view. We then form the mResNet by concatenating the outputs of each ResNet at the second to last layer, followed by a final, fully connected, layer. The resulting mResNet is trained in an end-to-end fashion to produce a case-based mammogram classifier that has the potential to be used in breast screening programs. We empirically show on the publicly available INbreast dataset, that the proposed mResNet classifies mammograms into malignant or normal/benign with an AUC of 0.8.
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©2017 IEEE