AAI_2025_Capstone_Chronicles_Combined

Looking ahead, several avenues remain for refining and advancing this research toward

real-world deployment. Further tuning of StyleGAN-generated datasets paired with VGG-16

could involve experiments with larger, more diverse datasets and the application of

advanced hyperparameter optimization techniques, such as Bayesian optimization or

population-based training. To prepare these models for clinical use, rigorous validation

protocols must be adopted, including stratified k-fold cross-validation using geographically

and demographically distinct datasets, stress-testing models under edge case and low

quality imaging scenarios, and benchmarking against expert radiologists. In parallel,

regulatory considerations such as HIPAA compliance, reproducibility standards, and

model interpretability requirements must be addressed. Integration of explainability

frameworks like Grad-CAM or SHAP is also essential to provide clinicians with interpretable

justifications for model predictions. Only by systematically addressing these technical,

ethical, and regulatory dimensions can the proposed system be considered a viable

candidate for clinical decision support in diagnostic radiology.

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