Simulated image-specific microcalcification clusters and associated mass enhancement to enhance training of a deep learning model for cancer detection in contrast-enhanced mammography

We present an automated method to generate synthetic contrast-enhanced mammography cases with simulated microcalcification clusters. This method accounts for existing textures in the breast, with the simulated clusters inserted in the low-energy image. In parallel, potential mass-like enhancement is modelled from real values in the recombined image. The same deep learning model was trained with different amounts and ratios of real and synthetic data. When trained with real data only, malignant masses are more often correctly detected and classified than malignant microcalcification clusters. The addition of synthetic data with simulated clusters during training could increase detection sensitivity for all types of malignant lesions and maintained similar levels of AUC for classification. This enhanced performance was consistent on both internal and external test sets. These findings demonstrate the potential applicability of synthetic data to enhance deep learning models, especially when real data are scarce or imbalanced.