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Our purpose is to develop a breast lesion simulator and remover using Cycle-GAN that can simulate plausible breast lesions in normal mammograms and remove existing lesions from mammograms. We curated 10310 screening mammograms from 4832 women (2416 normal/healthy and 2416 with recalled lesions). We divided our dataset into development and testing with a ratio of 8:2. We segmented 400 by 400 pixel patches, containing lesions from recalled cases and the same sized patches from the center of a normal breast mammogram. CycleGAN consists of two GANs and each GAN works as a lesion simulator and lesion remover when we train it on recalled lesions and normal patches. We used the development set to optimize the Cycle-GAN. Once trained, we applied Cycle-GAN on our test set to create lesion-simulated-normal controls and lesion-removed-recalled cases. After that, we trained ResNet18 as our CADe algorithm and tested it on our test set under four conditions: 1) original images (ResNet18Control), 2) original lesion cases + lesion simulated normal (ResNet18LSimul), 3) lesion removed + original normal controls (ResNet18LRemov), and 4) lesion removed + lesion simulated normal (ResNet18LSimul+LRemov). The test AUCs of ResNet18Control, ResNet18LSimul, ResNet18LRemov, and ResNet18LSimul+LRemov were 0.92, 0.79, 0.79, and 0.6, and their pairwise differences were statistically significant (p-value ¡ 0.0001), except the difference between ResNet18LSimul and ResNet18LRemov. Cycle-GAN can add plausible lesions to normal mammograms and remove existing lesions from mammograms such that the resulting images confused the computer-aided detection algorithm trained on the original mammograms.
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