Abstract
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 \(30{\times }\). 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.
Z. He and W. Liu—Equal contribution.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
Pretrained weights are downloaded from https://huggingface.co/runwayml/stable-diffusion-v1-5.
- 2.
Available at https://github.com/zhenqi-he/ArtiFusion.
- 3.
Available at https://github.com/zhenqi-he/transnuseg.
References
Babu, N.A., Anjuga, E.S., Masthan, K., Rajesh, E.: Artifacts in histopathology–a review. Indian Journal of Forensic Medicine and Toxicology (2020)
Creswell, A., White, T., Dumoulin, V., Arulkumaran, K., Sengupta, B., Bharath, A.A.: Generative adversarial networks: An overview. IEEE Signal Processing Magazine (2018)
Faragallah, O.S., El-Hoseny, H., El-Shafai, W., El-Rahman, W.A., El-Sayed, H.S., El-Rabaie, E.S.M., El-Samie, F.E.A., Geweid, G.G.N.: A comprehensive survey analysis for present solutions of medical image fusion and future directions. IEEE Access (2021)
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial networks. Communications of the ACM (2020)
Gurcan, M.N., Boucheron, L.E., Can, A., Madabhushi, A., Rajpoot, N.M., Yener, B.: Histopathological image analysis: A review. IEEE Reviews in Biomedical Engineering (2009)
Han, D., Yun, S., Heo, B., Yoo, Y.: Rexnet: Diminishing representational bottleneck on convolutional neural network. CVPR (2021)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. CVPR (2016)
He, Z., He, J., Ye, J., Shen, Y.: Artifact restoration in histology images with diffusion probabilistic models. In: MICCAI (2023)
He, Z., Unberath, M., Ke, J., Shen, Y.: Transnuseg: A lightweight multi-task transformer for nuclei segmentation. In: MICCAI (2023)
Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. CoRR (2020)
Ke, J., Liu, K., Sun, Y., Xue, Y., Huang, J., Lu, Y., Dai, J., Chen, Y., Han, X., Shen, Y., Shen, D.: Artifact detection and restoration in histology images with stain-style and structural preservation. IEEE Transactions on Medical Imaging (2023)
Ke, J., Lu, Y., Shen, Y., Zhu, J., Zhou, Y., Huang, J., Yao, J., Liang, X., Guo, Y., Wei, Z., Liu, S., Huang, Q., Jiang, F., Shen, D.: Clusterseg: A crowd cluster pinpointed nucleus segmentation framework with cross-modality datasets. Medical Image Analysis (2023)
Kingma, D.P., Welling, M.: An introduction to variational autoencoders. Foundations and Trends in Machine Learning (2019)
Litjens, G., Bandi, P., Ehteshami Bejnordi, B., Geessink, O., Balkenhol, M., Bult, P., Halilovic, A., Hermsen, M., van de Loo, R., Vogels, R., et al.: 1399 h &e-stained sentinel lymph node sections of breast cancer patients: the camelyon dataset. GigaScience (2018)
Liu, Z., Mao, H., Wu, C.Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2022)
von Platen, P., Patil, S., Lozhkov, A., Cuenca, P., Lambert, N., Rasul, K., Davaadorj, M., Wolf, T.: Diffusers: State-of-the-art diffusion models (2022)
Rombach, R., Blattmann, A., Lorenz, D., Esser, P., Ommer, B.: High-resolution image synthesis with latent diffusion models. CVPR (2022)
Seoane, J., Varela-Centelles, P., Ramírez, J., Cameselle-Teijeiro, J., Romero, M.: Artefacts in oral incisional biopsies in general dental practice: a pathology audit. Oral diseases (2004)
Song, Y., Sohl-Dickstein, J., Kingma, D.P., Kumar, A., Ermon, S., Poole, B.: Score-based generative modeling through stochastic differential equations. In: ICLR (2021)
Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2020)
Taqi, S.A., Sami, S.A., Sami, L.B., Zaki, S.A.: A review of artifacts in histopathology. Journal of oral and maxillofacial pathology: JOMFP (2018)
Verma, R., etc.: Monusac2020: A multi-organ nuclei segmentation and classification challenge. IEEE Transactions on Medical Imaging (2021)
Wang, C.Y., Mark Liao, H.Y., Wu, Y.H., Chen, P.Y., Hsieh, J.W., Yeh, I.H.: Cspnet: A new backbone that can enhance learning capability of cnn. In: CVPR Workshops (2020)
Wang, N.C., Kaplan, J., Lee, J., Hodgin, J., Udager, A., Rao, A.: Stress testing pathology models with generated artifacts. Journal of Pathology Informatics (2021)
Wang, Z., Bovik, A., Sheikh, H., Simoncelli, E.: Image quality assessment: from error visibility to structural similarity. IEEE TIP (2004)
Zhang, L., Zhang, L., Mou, X., Zhang, D.: Fsim: A feature similarity index for image quality assessment. IEEE TIP (2011)
Zhang, Y., Sun, Y., Li, H., Zheng, S., Zhu, C., Yang, L.: Benchmarking the robustness of deep neural networks to common corruptions in digital pathology. In: MICCAI. Springer (2022)
Zhu, J.Y., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networkss. ICCV (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Ethics declarations
Disclosure of Interests
The authors have no competing interests to declare that are relevant to the content of this article.
Rights and permissions
Copyright information
© 2025 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
He, Z., Liu, W., Yin, M., Han, K. (2025). LatentArtiFusion: An Effective and Efficient Histological Artifacts Restoration Framework. In: Mukhopadhyay, A., Oksuz, I., Engelhardt, S., Mehrof, D., Yuan, Y. (eds) Deep Generative Models. DGM4MICCAI 2024. Lecture Notes in Computer Science, vol 15224. Springer, Cham. https://doi.org/10.1007/978-3-031-72744-3_20
Download citation
DOI: https://doi.org/10.1007/978-3-031-72744-3_20
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-72743-6
Online ISBN: 978-3-031-72744-3
eBook Packages: Computer ScienceComputer Science (R0)
