Simulating breast mammogram using Conditional Generative Adversarial Network: application towards finding mammographically-occult cancer

We are developing a computerized method to detect mammographically-occult (MO) breast cancers in screening mammograms. The technique exploits asymmetries between mammograms of the left and right breasts. In this study we investigated whether using a conditional generative adversarial network (CGAN) to produce simulated images of the contralateral breast can provide additional information as to breast cancer occurrence (supplementing the left-right mammogram comparison). We trained the CGAN using 1366 normal screening mammograms to simulate the opposite breast, by using the left-right pair as input. After training, we found increased similarity (mean squared error (MSE) and 2D-correlation) between the pair of simulated contralateral and actual (real) mammograms (SR) compared to that of the pair of actual (real left and real right) mammograms (RR). We then applied the CGAN on the independent screening mammogram dataset of 333 women with dense breasts, containing 97 unilateral MO cancer. We computed the similarity measures on the SR and RR pairs. The similarity between the SR pairs of MO cases was smaller than that of controls, while the similarity between the RR pairs of MO cases and controls was similar to each other. We trained a logistic regression classifier using similarity measures as markers for finding MO cancer. Using 10-folder cross-validation, the AUC of the SR+RR classifier was 0.67±0.09 compared to 0.57±0.1 for the RR classifier (p=0.032). We conclude that by comparing a mammogram with simulated images can provide additional information than obtained by comparing pairs of actual mammograms.