Automatic examination of thin-prep cytologic test (TCT) slides can assist pathologists in finding cervical abnormality for accurate and efficient cancer screening. Current solutions mostly need to localize suspicious cells and classify abnormality based on local patches, concerning the fact that whole slide images of TCT are extremely large. It thus requires many annotations of normal and abnormal cervical cells, to supervise the training of the patch-level classifier for promising performance. In this paper, we propose CellGAN to synthesize cytopathological images of various cervical cell types for augmenting patch-level cell classification. Built upon a lightweight backbone, CellGAN is equipped with a non-linear class mapping network to effectively incorporate cell type information into image generation. We also propose the Skip-layer Global Context module to model the complex spatial relationship of the cells, and attain high fidelity of the synthesized images through adversarial learning. Our experiments demonstrate that CellGAN can produce visually plausible TCT cytopathological images for different cell types. We also validate the effectiveness of using CellGAN to greatly augment patch-level cell classification performance.

翻译：薄层液基细胞学检查（TCT）玻片的自动分析可辅助病理学家发现宫颈异常，以实现准确高效的癌症筛查。由于TCT全切片图像尺寸极大，当前解决方案大多需要定位可疑细胞并根据局部图像块进行异常分类。这需要大量正常与异常宫颈细胞的标注来监督训练图像块级分类器，以获得令人满意的性能。本文提出CellGAN以合成多种宫颈细胞类型的细胞病理图像，用于增强图像块级细胞分类。基于轻量级骨干网络，CellGAN配备非线性类映射网络，将细胞类型信息有效融入图像生成。我们还提出跳层全局上下文模块以建模细胞间复杂空间关系，并通过对抗学习实现合成图像的高保真度。实验表明，CellGAN能够为不同细胞类型生成视觉上可信的TCT细胞病理图像。我们还验证了利用CellGAN显著增强图像块级细胞分类性能的有效性。