  Breast cancer is the second leading cause of cancer death among women
worldwide. Nevertheless, it is also one of the most treatable malignances if
detected early. Screening for breast cancer with digital mammography (DM) has
been widely used. However it demonstrates limited sensitivity for women with
dense breasts. An emerging technology in the field is contrast-enhanced digital
mammography (CEDM), which includes a low energy (LE) image similar to DM, and a
recombined image leveraging tumor neoangiogenesis similar to breast magnetic
resonance imaging (MRI). CEDM has shown better diagnostic accuracy than DM.
While promising, CEDM is not yet widely available across medical centers. In
this research, we propose a Shallow-Deep Convolutional Neural Network (SD-CNN)
where a shallow CNN is developed to derive "virtual" recombined images from LE
images, and a deep CNN is employed to extract novel features from LE,
recombined or "virtual" recombined images for ensemble models to classify the
cases as benign vs. cancer. To evaluate the validity of our approach, we first
develop a deep-CNN using 49 CEDM cases collected from Mayo Clinic to prove the
contributions from recombined images for improved breast cancer diagnosis (0.86
in accuracy using LE imaging vs. 0.90 in accuracy using both LE and recombined
imaging). We then develop a shallow-CNN using the same 49 CEDM cases to learn
the nonlinear mapping from LE to recombined images. Next, we use 69 DM cases
collected from the hospital located at Zhejiang University, China to generate
"virtual" recombined images. Using DM alone provides 0.91 in accuracy, whereas
SD-CNN improves the diagnostic accuracy to 0.95.
