Abstract：
With the development of digital pathology, deep learning is increasingly being applied to endometrial cell morphology analysis for cancer screening. And cytology images with different staining may degrade the performance of these analysis algorithms. To address the impact of staining patterns, many strategies have been proposed and hematoxylin and eosin (H&E) images have been transferred to other staining styles. However, none of the existing methods are able to generate realistic cytological images with preserved cellular layout, and many important clinical structural information is lost. To address the above issues, we propose a different staining transformation model, CytoGAN, which can quickly and realistically generate images with different staining styles. It includes a novel structure preservation module that preserves the cell structure well, even if the resolution or cell size between the source and target domains do not match. Meanwhile, a stain adaptive module is designed to help the model generate realistic and high-quality endometrial cytology images. We compared our model with ten state-of-the-art stain transformation models and evaluated by two pathologists. Furthermore, in the downstream endometrial cancer classification task, our algorithm improves the robustness of the classification model on multimodal datasets, with more than 20 % improvement in accuracy. We found that generating specified specific stains from existing H&E images improves the diagnosis of endometrial cancer. Our code will be available on github.
摘要：
随着数字病理学的发展,深度学习越来越多地应用于子宫内膜细胞形态学分析以进行癌症筛查。并且具有不同染色的细胞学图像可能降低这些分析算法的性能。为了解决染色模式的影响，已经提出了许多策略，并且苏木精和伊红（H＆E）图像已被转移到其他染色样式。然而,现有的方法都不能生成具有保留的细胞布局的真实细胞学图像，许多重要的临床结构信息丢失。为了解决上述问题，我们提出了一种不同的染色转化模型，CytoGAN,它可以快速，逼真地生成具有不同染色样式的图像。它包括一个新颖的结构保存模块，可以很好地保存细胞结构，即使源和目标域之间的分辨率或单元格大小不匹配。同时,染色自适应模块被设计来帮助模型生成真实和高质量的子宫内膜细胞学图像。我们将我们的模型与十种最先进的染色转化模型进行了比较，并由两名病理学家进行了评估。此外，在下游子宫内膜癌分类任务中，我们的算法提高了分类模型在多模态数据集上的鲁棒性，精度提高20%以上。我们发现，从现有的H&E图像生成特定的特定染色改善了子宫内膜癌的诊断。我们的代码将在github上可用。
