Overview

Synthetic data generation creates realistic images to augment training datasets. It addresses class imbalance and rare pathology scarcity. Synthetic data supports model robustness and generalization.

Techniques

Generative adversarial networks and diffusion models produce high fidelity synthetic images. Conditioning on clinical labels enables targeted augmentation. Quality assessment ensures realism and utility.

Applications

Synthetic data aids training for rare tumors and underrepresented populations. It reduces need for extensive manual annotation and accelerates model development. Careful validation prevents synthetic artifacts from biasing models.

Ethical Considerations

Synthetic data must be labeled and tracked to avoid misuse. Transparency about synthetic content supports reproducibility and trust. Regulatory guidance on synthetic data use is emerging.