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.

具有分布匹配损失的未配对域转换模型（例如 CycleGAN）现在被广泛用于转移医学图像中的域。但是，使用 CycleGAN 合成医学图像可能会导致对医学状况的误诊，因为它可能会产生不想要的特征，尤其是在存在数据偏差的情况下。这可能会改变输入图像的原始类别，这是一个非常严重的问题。在本文中，我们为 CycleGAN 引入了改进的分布匹配损失，以消除疟疾数据集上的特征幻觉。在疟疾数据集的上下文中，无意的特征幻觉可能会引入类似于寄生虫的刻面或在翻译后移除寄生虫。我们提出的方法使我们能够转移疟疾数据集的域，而不会改变相应类别的风险。我们已经提供了实验证据，表明我们修改后的损失通过保留原始类标签显着减少了特征幻觉。与针对翻译域的基线（经典 CycleGAN）相比，实验结果更好。我们相信，我们的方法将加快开发对临床使用安全的无监督非配对 GAN 的过程。