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Review
. 2019 Feb;212(2):300-307.
doi: 10.2214/AJR.18.20392.

New Frontiers: An Update on Computer-Aided Diagnosis for Breast Imaging in the Age of Artificial Intelligence

Affiliations
Review

New Frontiers: An Update on Computer-Aided Diagnosis for Breast Imaging in the Age of Artificial Intelligence

Yiming Gao et al. AJR Am J Roentgenol. 2019 Feb.

Erratum in

  • Corrections.
    [No authors listed] [No authors listed] AJR Am J Roentgenol. 2019 Mar;212(3):712. doi: 10.2214/AJR.19.21198. AJR Am J Roentgenol. 2019. PMID: 30793944 No abstract available.

Abstract

Objective: The purpose of this article is to compare traditional versus machine learning-based computer-aided detection (CAD) platforms in breast imaging with a focus on mammography, to underscore limitations of traditional CAD, and to highlight potential solutions in new CAD systems under development for the future.

Conclusion: CAD development for breast imaging is undergoing a paradigm shift based on vast improvement of computing power and rapid emergence of advanced deep learning algorithms, heralding new systems that may hold real potential to improve clinical care.

Keywords: artificial intelligence; breast; computer-aided detection; computer-aided diagnosis; mammography; texture analysis.

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Figures

Fig. 1—
Fig. 1—
46-year-old woman with heterogeneously dense breast tissue. A and B, Digital mammograms of left breast in mediolateral oblique (A) and craniocaudal (B) views show traditional computer-aided detection markings highlighting masses in asterisks (solid arrows) and calcifications in triangles (dashed arrows).
Fig. 2—
Fig. 2—
Overview of multiview deep convolutional network (DCN) [19]. DCN refers to series of convolutional and pooling layers applied separately to each mammographic view. It is described in detail in Figure 3. Arrow indicates direction of information flow. L = left, R = right, CC = craniocaudal, MLO = mediolateral oblique.
Fig. 3—
Fig. 3—
Description of subnetwork processing single view. It transforms one mammographic view into fixed size vector that can be concatenated with other vectors for remaining views. For convolutional layers, kernel size denotes size of pattern that layer is extracting. For pooling layers, it denotes size of area over which average or maximum is computed. Stride denotes what is space between applications of convolution or pooling. Number of feature maps indicates how many different patterns network is extracting within each layer. Global average pooling is also type of pooling layer but it acts on entire image. Its purpose is to reduce size of feature maps into single vector.
Fig. 4—
Fig. 4—
Examples of visualization of decisions made by network. A and B, 42-year-old woman (BI-RADS category 0, A) and 51-year-old woman (BI-RADS category 2, B). Both left-hand images show breast with possible suspicious findings (red arrows, A; blue arrows, B). Both right-hand images are corresponding images of same breast with regions of images (highlighted in red) that influence confidence of predictions of neural network. Dashed lines in B denote scar markers overlying left breast.

References

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