论文标题
伪健康的综合，病理学分解和对抗性学习
Pseudo-healthy synthesis with pathology disentanglement and adversarial learning
论文作者
论文摘要
伪健康的合成是从病理学创建特定主题的“健康”形象的任务。这些图像可能有助于诸如病理和疾病引起的异常检测和理解变化之类的任务。在本文中，我们提出了一种模型，该模型被鼓励将病理信息从看起来健康的信息中解脱出来。我们将似乎健康的东西弄清楚，疾病是分割图，然后由网络重新组合以重建输入疾病图像。我们使用配对或未配对的设置对手进行对流训练模型，在该设置中，我们将疾病图像和地图配对。我们在人类的研究中进行了定量和主观的方式，使用几个标准评估了伪健康图像的质量。我们在一系列实验中显示，在小岛，小子和CAM-CAN数据集上执行，我们的方法比文献中的几种基准和方法更好。我们还表明，由于训练过程更好，我们可以在周围组织上恢复由疾病引起的变形。我们的实施可在https://github.com/xiat0616/pseudo-healthyy-synthesis中公开获得。本文已通过医学图像分析接受：https：//doi.org/10.1016/j.media.2020.101719。
Pseudo-healthy synthesis is the task of creating a subject-specific `healthy' image from a pathological one. Such images can be helpful in tasks such as anomaly detection and understanding changes induced by pathology and disease. In this paper, we present a model that is encouraged to disentangle the information of pathology from what seems to be healthy. We disentangle what appears to be healthy and where disease is as a segmentation map, which are then recombined by a network to reconstruct the input disease image. We train our models adversarially using either paired or unpaired settings, where we pair disease images and maps when available. We quantitatively and subjectively, with a human study, evaluate the quality of pseudo-healthy images using several criteria. We show in a series of experiments, performed on ISLES, BraTS and Cam-CAN datasets, that our method is better than several baselines and methods from the literature. We also show that due to better training processes we could recover deformations, on surrounding tissue, caused by disease. Our implementation is publicly available at https://github.com/xiat0616/pseudo-healthy-synthesis. This paper has been accepted by Medical Image Analysis: https://doi.org/10.1016/j.media.2020.101719.
