组织学图像中的伪影修复：基于扩散概率模型
Artifact Restoration in Histology Images with Diffusion Probabilistic Models
AI总结：
本文提出ArtiFusion，首个基于去噪扩散概率模型的组织学图像伪影修复方法，通过逐步去噪和Swin-Transformer架构，有效保留组织结构和染色风格。
摘要：
组织学全切片图像（WSIs）通常会受到伪影的影响，如组织折叠和气泡，这会增加病理学家和计算机辅助诊断（CAD）系统的检查难度。现有的伪影图像修复方法局限于生成对抗网络（GANs），其中修复过程被表述为图像到图像的转换。这些方法容易遭受模式崩溃和染色风格意外迁移的问题，导致修复图像不令人满意且不真实。创新性地，我们首次尝试使用去噪扩散概率模型进行组织学伪影修复，即ArtiFusion。具体来说，ArtiFusion将伪影区域修复表述为一个逐步去噪过程，其训练仅依赖于无伪影图像以简化训练复杂性。此外，为了在区域伪影修复中捕捉局部-全局相关性，设计了一种新颖的Swin-Transformer去噪架构，并配有时间令牌方案。我们的广泛评估证明了ArtiFusion作为组织学分析预处理方法的有效性，它能够在修复过程中成功保留无伪影区域的组织结构和染色风格。代码可在https://github.com/zhenqi-he/ArtiFusion获取。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
