Histological whole slide images (WSIs) can be usually compromised by artifacts, such as tissue folding and bubbles, which will increase the examination difficulty for both pathologists and Computer-Aided Diagnosis (CAD) systems. Existing approaches to restoring artifact images are confined to Generative Adversarial Networks (GANs), where the restoration process is formulated as an image-to-image transfer. Those methods are prone to suffer from mode collapse and unexpected mistransfer in the stain style, leading to unsatisfied and unrealistic restored images. Innovatively, we make the first attempt at a denoising diffusion probabilistic model for histological artifact restoration, namely ArtiFusion.Specifically, ArtiFusion formulates the artifact region restoration as a gradual denoising process, and its training relies solely on artifact-free images to simplify the training complexity.Furthermore, to capture local-global correlations in the regional artifact restoration, a novel Swin-Transformer denoising architecture is designed, along with a time token scheme. Our extensive evaluations demonstrate the effectiveness of ArtiFusion as a pre-processing method for histology analysis, which can successfully preserve the tissue structures and stain style in artifact-free regions during the restoration. Code is available at https://github.com/zhenqi-he/ArtiFusion.

翻译：组织学全切片图像（WSIs）常因组织折叠、气泡等人为痕迹而受损，这会增加病理学家和计算机辅助诊断（CAD）系统的检查难度。现有的人工痕迹图像修复方法局限于生成对抗网络（GAN），其修复过程被形式化为图像到图像的转换。这些方法容易出现模式坍缩和染色风格意外误转换的问题，导致修复图像不理想且缺乏真实性。创新性地，我们首次将去噪扩散概率模型应用于组织学人工痕迹修复，命名为ArtiFusion。具体而言，ArtiFusion将人工痕迹区域修复表述为渐进式去噪过程，其训练仅依赖无人工痕迹图像以简化训练复杂度。此外，为捕捉区域人工痕迹修复中的局部-全局相关性，我们设计了一种新型基于Swin-Transformer的去噪架构，并引入时间令牌方案。大量评估实验表明，ArtiFusion作为组织学分析的预处理方法具有有效性，能够在修复过程中成功保留无人工痕迹区域的组织结构和染色风格。代码开源地址：https://github.com/zhenqi-he/ArtiFusion。