CycleGAN with Dynamic Criterion for Malaria Blood Cell Image Synthetization

We present a cycle-consistent adversarial network (Cycle GAN) with dynamic criterion to synthesize blood cells parasitized by malaria plasmodia. The result shows 100% of the synthetic images are correctly classified by the pretrained classifier compared to 99.61% of the real images, 76.6% generated by the Cycle GAN without the dynamic criterion. The average score of Frechet Inception Distance (FID) of the generated images by the enhanced Cycle GAN is 0.0043 (Std=0.0005), which is significantly lower than the FID score of the variational autoencoder (VAE) model (0.0085 (Std=0.0007)). We conclude that the new Cycle GAN model with dynamic criterion can generate high quality malaria infected blood cell images with good diversity. The new method provides new augmentation technique to enhance the image diversity where the acquisition of well-annotated images is highly restricted, and to improve the robustness of medical image automatic processing by deep neural networks.