论文标题
使用风格转移网络和对抗性损失的样式转移网络的组织病理染色转移
Histopathological Stain Transfer using Style Transfer Network with Adversarial Loss
论文作者
论文摘要
经过从单个实验室和/或扫描仪获得的组织病理学图像进行训练的深度学习模型，从而在其他具有不同染色方案的扫描仪/实验室获得的图像上具有较差的推理性能。近年来，已经进行了大量的研究，以解决图像染色标准化以解决此问题。在这项工作中，我们使用快速神经风格转移以及对抗性损失提出了一种新颖的染色标准化方法。我们还提出了基于高分辨率网络（HRNET）的新型染色传输发生器网络，该网络需要更少的训练时间，并提供良好的概括，并提供了参考染色和测试污渍的少数配对训练图像。该方法已在从8个不同实验室获得的整个幻灯片图像（WSI）上进行了测试，其中一个实验室的图像被视为参考染色。对该污渍进行了深度学习模型，并使用相应的污渍传输发生器网络将其余图像转移到了它上。实验表明，这种方法能够以良好的视觉质量成功执行染色归一化，并与不应用染色归一化相比提供了更好的推理性能。
Deep learning models that are trained on histopathological images obtained from a single lab and/or scanner give poor inference performance on images obtained from another scanner/lab with a different staining protocol. In recent years, there has been a good amount of research done for image stain normalization to address this issue. In this work, we present a novel approach for the stain normalization problem using fast neural style transfer coupled with adversarial loss. We also propose a novel stain transfer generator network based on High-Resolution Network (HRNet) which requires less training time and gives good generalization with few paired training images of reference stain and test stain. This approach has been tested on Whole Slide Images (WSIs) obtained from 8 different labs, where images from one lab were treated as a reference stain. A deep learning model was trained on this stain and the rest of the images were transferred to it using the corresponding stain transfer generator network. Experimentation suggests that this approach is able to successfully perform stain normalization with good visual quality and provides better inference performance compared to not applying stain normalization.
