论文标题
组织病理学图像中的伪影
Artifact Removal in Histopathology Images
论文作者
论文摘要
在组织病理学的临床环境中，经常出现全扫描图像（WSI）伪像，扭曲了感兴趣的区域，并对WSI分析产生了有害影响。图像到图像翻译网络（例如Cyclegans）原则上能够从未配对的数据中学习伪影删除功能。但是，我们确定了移除伪像的陈述问题，并提出了对Cyclegan的弱监督扩展，以解决这一问题。我们组装了一个包含TCGA数据库的工件和干净瓷砖的泛伴侣数据集。有希望的结果突出了我们方法的健全性。
In the clinical setting of histopathology, whole-slide image (WSI) artifacts frequently arise, distorting regions of interest, and having a pernicious impact on WSI analysis. Image-to-image translation networks such as CycleGANs are in principle capable of learning an artifact removal function from unpaired data. However, we identify a surjection problem with artifact removal, and propose an weakly-supervised extension to CycleGAN to address this. We assemble a pan-cancer dataset comprising artifact and clean tiles from the TCGA database. Promising results highlight the soundness of our method.
