  Imbalanced image datasets are commonly available in the domain of biomedical
image analysis. Biomedical images contain diversified features that are
significant in predicting targeted diseases. Generative Adversarial Networks
(GANs) are utilized to address the data limitation problem via the generation
of synthetic images. Training challenges such as mode collapse,
non-convergence, and instability degrade a GAN's performance in synthesizing
diversified and high-quality images. In this work, MSG-SAGAN, an
attention-guided multi-scale gradient GAN architecture is proposed to model the
relationship between long-range dependencies of biomedical image features and
improves the training performance using a flow of multi-scale gradients at
multiple resolutions in the layers of generator and discriminator models. The
intent is to reduce the impact of mode collapse and stabilize the training of
GAN using an attention mechanism with multi-scale gradient learning for
diversified X-ray image synthesis. Multi-scale Structural Similarity Index
Measure (MS-SSIM) and Frechet Inception Distance (FID) are used to identify the
occurrence of mode collapse and evaluate the diversity of synthetic images
generated. The proposed architecture is compared with the multi-scale gradient
GAN (MSG-GAN) to assess the diversity of generated synthetic images. Results
indicate that the MSG-SAGAN outperforms MSG-GAN in synthesizing diversified
images as evidenced by the MS-SSIM and FID scores.
