论文标题
乳房组织病理学有丝分裂检测的虚拟染色
Virtual staining for mitosis detection in Breast Histopathology
论文作者
论文摘要
我们提出了一种基于生成对抗网络的虚拟染色方法，以将乳腺癌组织的组织病理学图像从H＆E染色映射到PHH3，反之亦然。我们使用所得的合成图像来构建卷积神经网络（CNN）以自动检测有丝分裂数字，这是一种用于常规乳腺癌诊断和分级用于常规的预后生物标志物。我们提出了几种场景，其中通过合成生成的组织病理学图像训练的CNN在与实际图像训练的基线模型相当甚至更好。我们讨论了此应用程序的潜力，即不需要手动注释而扩展培训样本的数量。
We propose a virtual staining methodology based on Generative Adversarial Networks to map histopathology images of breast cancer tissue from H&E stain to PHH3 and vice versa. We use the resulting synthetic images to build Convolutional Neural Networks (CNN) for automatic detection of mitotic figures, a strong prognostic biomarker used in routine breast cancer diagnosis and grading. We propose several scenarios, in which CNN trained with synthetically generated histopathology images perform on par with or even better than the same baseline model trained with real images. We discuss the potential of this application to scale the number of training samples without the need for manual annotations.
