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. 2019 Jun 6;20(Suppl 11):281.
doi: 10.1186/s12859-019-2823-4.

Deep Convolutional Neural Networks for Mammography: Advances, Challenges and Applications

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Free PMC article

Deep Convolutional Neural Networks for Mammography: Advances, Challenges and Applications

Dina Abdelhafiz et al. BMC Bioinformatics. .
Free PMC article

Abstract

Background: The limitations of traditional computer-aided detection (CAD) systems for mammography, the extreme importance of early detection of breast cancer and the high impact of the false diagnosis of patients drive researchers to investigate deep learning (DL) methods for mammograms (MGs). Recent breakthroughs in DL, in particular, convolutional neural networks (CNNs) have achieved remarkable advances in the medical fields. Specifically, CNNs are used in mammography for lesion localization and detection, risk assessment, image retrieval, and classification tasks. CNNs also help radiologists providing more accurate diagnosis by delivering precise quantitative analysis of suspicious lesions.

Results: In this survey, we conducted a detailed review of the strengths, limitations, and performance of the most recent CNNs applications in analyzing MG images. It summarizes 83 research studies for applying CNNs on various tasks in mammography. It focuses on finding the best practices used in these research studies to improve the diagnosis accuracy. This survey also provides a deep insight into the architecture of CNNs used for various tasks. Furthermore, it describes the most common publicly available MG repositories and highlights their main features and strengths.

Conclusions: The mammography research community can utilize this survey as a basis for their current and future studies. The given comparison among common publicly available MG repositories guides the community to select the most appropriate database for their application(s). Moreover, this survey lists the best practices that improve the performance of CNNs including the pre-processing of images and the use of multi-view images. In addition, other listed techniques like transfer learning (TL), data augmentation, batch normalization, and dropout are appealing solutions to reduce overfitting and increase the generalization of the CNN models. Finally, this survey identifies the research challenges and directions that require further investigations by the community.

Keywords: Breast cancer; Classification; Computer-aided detection (CAD); Convolutional neural networks (CNNs); Deep learning (DL); Feature detection; Machine learning (ML); Mammograms (MGs); Transfer learning (TL).

Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
A breakdown of the studies included in this survey in the year of publication grouped by their neural network task. Since 2016 the number of studies on CNN for MGs has increased significantly
Fig. 2
Fig. 2
The CNN architecture is a stack of Convolutional layer (Conv), Nonlinear layer (e.g. ReLU), Pooling layer (Pool), and a Loss function (e.g. SVM/Softmax) on the last (Fully connected) layer. The output can be a single class (e.g. Normal, Benign, Malignant)
Fig. 3
Fig. 3
Statistics for the included studies

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