Deep learning beyond cats and dogs: recent advances in diagnosing breast cancer with deep neural networks

Abstract

Deep learning has demonstrated tremendous revolutionary changes in the computing industry and its effects in radiology and imaging sciences have begun to dramatically change screening paradigms. Specifically, these advances have influenced the development of computer-aided detection and diagnosis (CAD) systems. These technologies have long been thought of as "second-opinion" tools for radiologists and clinicians. However, with significant improvements in deep neural networks, the diagnostic capabilities of learning algorithms are approaching levels of human expertise (radiologists, clinicians etc.), shifting the CAD paradigm from a "second opinion" tool to a more collaborative utility. This paper reviews recently developed CAD systems based on deep learning technologies for breast cancer diagnosis, explains their superiorities with respect to previously established systems, defines the methodologies behind the improved achievements including algorithmic developments, and describes remaining challenges in breast cancer screening and diagnosis. We also discuss possible future directions for new CAD models that continue to change as artificial intelligence algorithms evolve.

Figure 1.

Comparison of conventional machine learning approach vs deep learning based approaches. ROI, Region of interest.

Figure 2.

Statistical distribution of 28 deep learning papers selected for this review. The distribution regarding imaging modalities (a), year of publication (b), deep learning applications (c), and type of publication (d) are shown.

Figure 3.

The connection between convolutional networks and the recognition pathway of the human visual cortex is illustrated. The recognition pathway in the visual cortex has multiple relays of information processing: retina, LGN, V1, V2, V4, PIT, AIT. Similarly, these relays of processing are represented in convolutional neural network layers. DoG, difference of Gaussians; LGN, lateral geniculate nucleus; LN, convolution—batch-normalization—RELU—max-pooling; RGC, retinal ganglion cell; T(*), transformation.