  In biomedical image analysis, data imbalance is common across several imaging
modalities. Data augmentation is one of the key solutions in addressing this
limitation. Generative Adversarial Networks (GANs) are increasingly being
relied upon for data augmentation tasks. Biomedical image features are
sensitive to evaluating the efficacy of synthetic images. These features can
have a significant impact on metric scores when evaluating synthetic images
across different biomedical imaging modalities. Synthetically generated images
can be evaluated by comparing the diversity and quality of real images.
Multi-scale Structural Similarity Index Measure and Cosine Distance are used to
evaluate intra-class diversity, while Frechet Inception Distance is used to
evaluate the quality of synthetic images. Assessing these metrics for
biomedical and non-biomedical imaging is important to investigate an informed
strategy in evaluating the diversity and quality of synthetic images. In this
work, an empirical assessment of these metrics is conducted for the Deep
Convolutional GAN in a biomedical and non-biomedical setting. The diversity and
quality of synthetic images are evaluated using different sample sizes. This
research intends to investigate the variance in diversity and quality across
biomedical and non-biomedical imaging modalities. Results demonstrate that the
metrics scores for diversity and quality vary significantly across
biomedical-to-biomedical and biomedical-to-non-biomedical imaging modalities.
