3C-GAN: class-consistent CycleGAN for malaria domain adaptation model

Unpaired domain translation models with distribution matching loss such as CycleGAN are now widely being used to shift domain in medical images. However, synthesizing medical images using CycleGAN can lead to misdiagnosis of a medical condition as it might hallucinate unwanted features, especially if theres a data bias. This can potentially change the original class of the input image, which is a very serious problem. In this paper, we have introduced a modified distribution matching loss for CycleGAN to eliminate feature hallucination on the malaria dataset. In the context of the malaria dataset, unintentional feature hallucination may introduce a facet that resembles a parasite or remove the parasite after the translation. Our proposed approach has enabled us to shift the domain of the malaria dataset without the risk of changing their corresponding class. We have presented experimental evidence that our modified loss significantly reduced feature hallucination by preserving original class labels. The experimental results are better in comparison to the baseline (classic CycleGAN) that targets the translating domain. We believe that our approach will expedite the process of developing unsupervised unpaired GAN that is safe for clinical use.