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| Main Authors: | , , , |
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| Format: | Preprint |
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2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2407.20172 |
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| _version_ | 1866910545727193088 |
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| author | He, Zhenqi Liu, Wenrui Yin, Minghao Han, Kai |
| author_facet | He, Zhenqi Liu, Wenrui Yin, Minghao Han, Kai |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_20172 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | LatentArtiFusion: An Effective and Efficient Histological Artifacts Restoration Framework He, Zhenqi Liu, Wenrui Yin, Minghao Han, Kai Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition 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. |
| title | LatentArtiFusion: An Effective and Efficient Histological Artifacts Restoration Framework |
| topic | Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2407.20172 |