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In biomedical image analysis, data imbalance is common across several imagingmodalities. Data augmentation is one of the key solutions in addressing thislimitation. Generative Adversarial Networks (GANs) are increasingly beingrelied upon for data augmentation tasks. Biomedical image features aresensitive to evaluating the efficacy of synthetic images. These features canhave a significant impact on metric scores when evaluating synthetic imagesacross different biomedical imaging modalities. Synthetically generated imagescan be evaluated by comparing the diversity and quality of real images.Multi-scale Structural Similarity Index Measure and Cosine Distance are used toevaluate intra-class diversity, while Frechet Inception Distance is used toevaluate the quality of synthetic images. Assessing these metrics forbiomedical and non-biomedical imaging is important to investigate an informedstrategy in evaluating the diversity and quality of synthetic images. In thiswork, an empirical assessment of these metrics is conducted for the DeepConvolutional GAN in a biomedical and non-biomedical setting. The diversity andquality of synthetic images are evaluated using different sample sizes. Thisresearch intends to investigate the variance in diversity and quality acrossbiomedical and non-biomedical imaging modalities. Results demonstrate that themetrics scores for diversity and quality vary significantly acrossbiomedical-to-biomedical and biomedical-to-non-biomedical imaging modalities.

Assessing Intra-class Diversity and Quality of Synthetically Generated Images in a Biomedical and Non-biomedical Setting