LatentArtiFusion: An Effective and Efficient Histological Artifacts Restoration Framework
<|reference_start|>LatentArtiFusion: An Effective and Efficient Histological Artifacts Restoration Framework: 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.<|reference_end|>
@article{he2024latentartifusion:,
    title={LatentArtiFusion: An Effective and Efficient Histological Artifacts
  Restoration Framework},
    author={Zhenqi He, Wenrui Liu, Minghao Yin, Kai Han},
    journal={arXiv preprint arXiv:2407.20172},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.20172},
    primaryClass={eess.IV cs.AI cs.CV}
}