论文标题
使用生成对抗网络通过无监督的域增强域进行组织病理学染色不变性
Towards Histopathological Stain Invariance by Unsupervised Domain Augmentation using Generative Adversarial Networks
论文作者
论文摘要
监督深度学习方法在数字病理学中的应用受到限制，因为它们对域转移的敏感性。数字病理学是由于许多来源而容易出现高变异性的领域，包括评估几个连续的组织切片，这些组织切片用不同的染色方案染色。获得每种污渍的标签非常昂贵且耗时，因为它需要高水平的领域知识。在本文中，我们提出了一种基于对抗性图像到图像翻译的无监督的增强方法，该方法促进了对染色不变的监督卷积神经网络的培训。通过在一种常用的染色方式上训练网络，并将其应用于包括相应但染色不同的组织结构的图像，提出的方法比其他方法显示出显着改善。这些好处在七种不同的染色方式（PAS，Jones H＆E，CD68，Sirius Red，CD34，H＆E和CD3）中的肾小球分割问题中说明了这些好处，并且对学习表示的分析表明了它们的染色不变性。
The application of supervised deep learning methods in digital pathology is limited due to their sensitivity to domain shift. Digital Pathology is an area prone to high variability due to many sources, including the common practice of evaluating several consecutive tissue sections stained with different staining protocols. Obtaining labels for each stain is very expensive and time consuming as it requires a high level of domain knowledge. In this article, we propose an unsupervised augmentation approach based on adversarial image-to-image translation, which facilitates the training of stain invariant supervised convolutional neural networks. By training the network on one commonly used staining modality and applying it to images that include corresponding, but differently stained, tissue structures, the presented method demonstrates significant improvements over other approaches. These benefits are illustrated in the problem of glomeruli segmentation in seven different staining modalities (PAS, Jones H&E, CD68, Sirius Red, CD34, H&E and CD3) and analysis of the learned representations demonstrate their stain invariance.
