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Our previous study showed that GAN-generated artifacts frequently appear in GAN-generated mammograms. As GAN artifacts could affect the performance of downstream tasks (e.g., augmentation), it may be beneficial to develop algorithms for detecting GAN-generated mammographic artifacts. Thus, we developed classification and segmentation algorithms for GAN-generated mammographic artifacts to classify the case with artifacts and then segment those artifacts in the GAN-generated mammograms. Using our two internal screening datasets, we trained and tested a Conditional GAN (CGAN) algorithm to simulate breast mammograms. For CGAN training, we used 1366 normal (without cancer) right/left breast mammograms. We then tested our CGAN model on an independent dataset of mammograms from 333 women with dense breasts for possible CGAN artifacts. An experienced radiologist evaluated the CGAN-generated mammograms, identified cases with artifacts, and segmented them. For the development of the classification models for artifact detection, we split the second dataset into training and testing sets using an 8:2 ratio. Using the radiologist’s annotation as ground truth, we trained a classifier (DenseNet121) to identify the two most common artifacts in CGAN-simulated mammograms, checkerboard and non-smooth breast boundary artifacts. We then trained a Unet to segment artifacts precisely. Our classifier achieved an AUC of 0.67 for the checkerboard artifacts and an AUC of 0.78 for breast boundaries. Our segmentation algorithm achieved a dice score of 0.64 for the checkerboard artifact and 0.57 for the breast boundary artifact. We showed that it is possible to identify the mammograms with CGAN artifacts. More investigation is needed to improve the segmentation and classification of artifacts.
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