LatentArtiFusion：一种高效且高能的组织学伪影修复框架
LatentArtiFusion: An Effective and Efficient Histological Artifacts Restoration Framework
AI总结：
本文提出LatentArtiFusion框架，利用潜在扩散模型在低维空间进行组织学伪影修复，并引入区域伪影重建算法防止非伪影区错误迁移，速度提升超30倍且指标超GAN方法5%以上。
摘要：
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（GANs）和像素级扩散模型（Diffusion Models）的组织学伪影修复方法，存在性能局限和计算效率低下的问题。本文提出了一种新框架LatentArtiFusion，利用潜在扩散模型（LDM）以高性能和高计算效率重建组织学伪影。与传统像素级扩散框架不同，LatentArtiFusion在低维潜在空间中执行修复过程，显著提升了计算效率。此外，我们在潜在空间引入了一种新的区域伪影重建算法，以防止非伪影区域发生错误迁移，这使我们的方法区别于基于GAN的方法。通过在真实世界组织学数据集上的大量实验，LatentArtiFusion展现出卓越的速度，比最先进的像素级扩散框架快30倍以上。在多项评估指标上，它也始终比基于GAN的方法高出至少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。
英文摘要：
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.
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。
