Abstract
Automation in medical imaging is quite challenging due to the unavailability of annotated datasets and the scarcity of domain experts. In recent years, deep learning techniques have solved some complex medical imaging tasks like disease classification, important object localization, segmentation, etc. However, most of the task requires a large amount of annotated data for their successful implementation. To mitigate the shortage of data, different generative models are proposed for data augmentation purposes which can boost the classification performances. For this, different synthetic medical image data generation models are developed to increase the dataset. Unpaired image-to-image translation models here shift the source domain to the target domain. In the breast malignancy identification domain, FNAC is one of the low-cost low-invasive modalities normally used by medical practitioners. But availability of public datasets in this domain is very poor. Whereas, for automation of cytology images, we need a large amount of annotated data. Therefore synthetic cytology images are generated by translating breast histopathology samples which are publicly available. In this study, we have explored traditional image-to-image transfer models like CycleGAN, and Neural Style Transfer. Further, it is observed that the generated cytology images are quite similar to real breast cytology samples by measuring FID and KID scores.
Abstract (translated)
医学影像自动化领域是一个相当具有挑战性的任务，因为缺乏带注释的数据集和领域专家的数量。近年来，深度学习技术已经解决了一些复杂的医学影像任务，如疾病分类、重要目标定位、分割等。然而，大多数任务需要大量带注释的数据才能成功实现。为缓解数据不足，不同生成模型被提出用于数据增强，以提高分类性能。为此，不同类型的生成医学图像数据生成模型被开发以增加数据集。无配对图像到图像转换模型在这里将源域转移到目标域。在乳腺癌识别领域，FNAC是一种低成本、低侵入性的医学检查手段，但该领域的公开数据集非常缺乏。相反，用于细胞学图像自动化需要大量带注释的数据。因此，通过将乳腺癌病理学样本平移生成细胞学图像，实现了合成细胞学图像。在本研究中，我们探讨了传统图像到图像转移模型，如CycleGAN和Neural Style Transfer。此外，观察到生成的细胞学图像与真实乳腺癌细胞图像在FID和KID分数上相当相似。
URL
https://arxiv.org/abs/2403.10885
