  This study explores the utilization of Dermatoscopic synthetic data generated
through stable diffusion models as a strategy for enhancing the robustness of
machine learning model training. Synthetic data generation plays a pivotal role
in mitigating challenges associated with limited labeled datasets, thereby
facilitating more effective model training. In this context, we aim to
incorporate enhanced data transformation techniques by extending the recent
success of few-shot learning and a small amount of data representation in
text-to-image latent diffusion models. The optimally tuned model is further
used for rendering high-quality skin lesion synthetic data with diverse and
realistic characteristics, providing a valuable supplement and diversity to the
existing training data. We investigate the impact of incorporating newly
generated synthetic data into the training pipeline of state-of-art machine
learning models, assessing its effectiveness in enhancing model performance and
generalization to unseen real-world data. Our experimental results demonstrate
the efficacy of the synthetic data generated through stable diffusion models
helps in improving the robustness and adaptability of end-to-end CNN and vision
transformer models on two different real-world skin lesion datasets.
