论文标题
使用U-NET的H＆E染色归一化
H&E Stain Normalization using U-Net
论文作者
论文摘要
我们提出了一种基于修改的U-NET神经网络结构的新型苏木精和曙红（H＆E）染色归一化方法。与通常基于生成对抗网络（GAN）的以前的深度学习方法不同，我们采用教师学生的方法，并使用训练有素的Cyclegan生成的配对数据集来训练U-NET来执行染色标准化任务。通过实验，我们将我们的方法与最近的两种竞争方法分别进行了比较，即Cyclean和Stainnet，这是一种基于教师学生模型的轻量级方法。我们发现我们的方法更快，并且与Cyclegan相比，可以使用质量更高的更大图像处理。我们还与Stainnet进行了比较，发现我们的方法在定量和质量上提供了更好的结果。
We propose a novel hematoxylin and eosin (H&E) stain normalization method based on a modified U-Net neural network architecture. Unlike previous deep-learning methods that were often based on generative adversarial networks (GANs), we take a teacher-student approach and use paired datasets generated by a trained CycleGAN to train a U-Net to perform the stain normalization task. Through experiments, we compared our method to two recent competing methods, CycleGAN and StainNet, a lightweight approach also based on the teacher-student model. We found that our method is faster and can process larger images with better quality compared to CycleGAN. We also compared to StainNet and found that our method delivered quantitatively and qualitatively better results.
