Stage aware synthetic generation of high quality human blastocyst images using StyleGAN3 with multi level validation

The lack of large, balanced, and shareable biomedical imaging datasets remains a major barrier to the development, validation, and clinical translation of artificial intelligence systems. Synthetic data generation has emerged as a promising solution, yet its real-world value critically depends on rigorous validation beyond visual realism. In this study, we present and systematically evaluate a stage-aware generative framework based on StyleGAN3 for high-fidelity biomedical image synthesis, using human embryo development in Assisted Reproductive Technology. The StyleGAN3 models are trained to synthesize embryo images at the Start of Blastulation and Blastocyst stages, using a curated cohort of 96 high-quality embryos (selected from an original set of 704 time-lapse IVF videos based on combined ICM/TE Gardner grades of AA, AB, or BA). To assess reliability and practical utility, we introduce a multi-level evaluation protocol integrating distributional similarity (Frechet Inception Distance), frequency-domain spectral analysis, and downstream task-based validation. Experimental results show that the proposed models accurately reproduce global morphological characteristics, with consistently higher fidelity and stability at earlier developmental stages (FID = 52.1), while later-stage synthesis is challenged by intrinsic data imbalance and heterogeneity. Spectral analysis reveals partial smoothing of high-frequency details, highlighting limitations not captured by conventional generative metrics alone. Importantly, downstream experiments comparing real, mixed real-synthetic, and fully synthetic evaluation scenarios using ResNet50 and EfficientNet-B0, with classifiers trained exclusively on real data in all cases, show that synthetic images remain discriminatively informative and occupy a structured, non-degenerate feature space consistent with the learned morphological patterns of the classifiers. Overall, this work provides an impact-oriented validation of synthetic biomedical imaging data and underscores the necessity of task-level evaluation as a prerequisite for the responsible use of generative models in ART and broader healthcare applications.