Histological artifacts pose challenges for both pathologists and Computer-Aided Diagnosis (CAD) systems, leading to errors in analysis. Current approaches for histological artifact restoration, based on Generative Adversarial Networks (GANs) and pixel-level Diffusion Models, suffer from performance limitations and computational inefficiencies. In this paper, we propose a novel framework, LatentArtiFusion, which leverages the latent diffusion model (LDM) to reconstruct histological artifacts with high performance and computational efficiency. Unlike traditional pixel-level diffusion frameworks, LatentArtiFusion executes the restoration process in a lower-dimensional latent space, significantly improving computational efficiency. Moreover, we introduce a novel regional artifact reconstruction algorithm in latent space to prevent mistransfer in non-artifact regions, distinguishing our approach from GAN-based methods. Through extensive experiments on real-world histology datasets, LatentArtiFusion demonstrates remarkable speed, outperforming state-of-the-art pixel-level diffusion frameworks by more than 30X. It also consistently surpasses GAN-based methods by at least 5% across multiple evaluation metrics. Furthermore, we evaluate the effectiveness of our proposed framework in downstream tissue classification tasks, showcasing its practical utility. Code is available at https://github.com/bugs-creator/LatentArtiFusion.

翻译：组织学伪影给病理学家和计算机辅助诊断（CAD）系统带来了挑战，导致分析错误。当前基于生成对抗网络（GAN）和像素级扩散模型的组织学伪影修复方法存在性能局限性和计算效率低下的问题。本文提出了一种新颖的框架LatentArtiFusion，该框架利用潜在扩散模型（LDM）以高性能和计算效率重建组织学伪影。与传统的像素级扩散框架不同，LatentArtiFusion在低维潜在空间中执行修复过程，显著提高了计算效率。此外，我们引入了一种新颖的潜在空间区域伪影重建算法，以防止非伪影区域的误迁移，这使我们的方法有别于基于GAN的方法。通过对真实世界组织学数据集的大量实验，LatentArtiFusion展现出显著的速度优势，其性能超过最先进的像素级扩散框架30倍以上。在多个评估指标上，它也始终优于基于GAN的方法至少5%。此外，我们评估了所提框架在下游组织分类任务中的有效性，展示了其实用价值。代码可在 https://github.com/bugs-creator/LatentArtiFusion 获取。