Synthetic Mammography Image Generation Using DCGANs: Toward Self-Training of Artificial Intelligence Models

This study evaluated unconditional synthetic mammography generation with Deep Convolutional Generative Adversarial Networks (DCGANs). The dataset comprised 690 anonymized mammograms, equally distributed across BI-RADS 1–6 (115 images/category). All images were standardized to 512 × 512 pixels, reoriented to a common right-breast view, and prepared in two input domains: grayscale and color-mapped intensity encoding. Two models were compared: a standard DCGAN trained directly at 512 × 512, and a progressive DCGAN trained through discrete stages from 8 × 8 to 512 × 512. Training used PyTorch (Python 3.10), latent dimension = 100, Adam, learning rate = 2 × 10−4, β1 = 0.5, binary cross-entropy loss, and batch size = 1. Both models learned the low-frequency mammographic manifold, generating breast-like silhouettes and heterogeneous internal intensity distributions. However, the progressive DCGAN produced smoother contours, more coherent internal organization, and fewer grid/line artifacts than the standard model. Grayscale-only training showed weak learning, whereas the color-mapped domain improved structural recovery, although this advantage should be interpreted as computational rather than clinical. Late-epoch checkpoint analysis showed a structurally invariant generator with 43 tensors and 19,531,127 parameters; from epochs 89–100, relative checkpoint drift remained within 0.294–0.317%, with epochs 93–96 showing the most stable regime. Despite these advances, generated images still exhibited background speckle, coarse mottled texture, extra-anatomical bright structures, and limited diversity. Thus, the results support feasibility of synthetic mammography generation, but not yet clinically reliable synthetic data for direct AI training. The generated images should presently be regarded as exploratory complementary data pending expert, metric-based, and downstream validation.