A state-of-the-art review of diffusion model applications for microscopic image and micro-alike image analysis
Diffusion models, a class of deep learning models based on probabilistic generative processes, progressively transform data into noise and then reconstruct the original data through an inverse process. Recently, diffusion models have gained attention in microscopic image analysis for their ability t...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-07-01
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| Series: | Frontiers in Medicine |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmed.2025.1551894/full |
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| _version_ | 1850099573405515776 |
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| author | Yan Liu Tao Jiang Rui Li Lingling Yuan Marcin Grzegorzek Chen Li Xiaoyan Li |
| author_facet | Yan Liu Tao Jiang Rui Li Lingling Yuan Marcin Grzegorzek Chen Li Xiaoyan Li |
| author_sort | Yan Liu |
| collection | DOAJ |
| description | Diffusion models, a class of deep learning models based on probabilistic generative processes, progressively transform data into noise and then reconstruct the original data through an inverse process. Recently, diffusion models have gained attention in microscopic image analysis for their ability to process complex data, extract valuable information, and enhance image quality. This review provides an overview of diffusion models in microscopic images and micro-alike images, focusing on three commonly used models: DDPM, DDIM, and SDEs. We explore their applications in image generation, segmentation, denoising, classification, reconstruction and super-resolution. It shows their notable advantages, particularly in image generation and segmentation. Through simulating the imaging process of biological samples under the microscope, diffusion model can generate high-quality synthetic microscopic images. The generated images serve as a powerful tool for data augmentation when training deep learning models. Diffusion model also excels in microscopic image segmentation. It enables to accurately segment different cellular regions and tissue structures by simulating the interactions between pixels in an image. The review includes 31 papers, with 13 on image generation, nine on segmentation, and the remainder on other applications. We also discuss the strengths, limitations, and future directions for diffusion models in biomedical image processing. |
| format | Article |
| id | doaj-art-b7182c57ff374f4abc31734ebc7e3eee |
| institution | DOAJ |
| issn | 2296-858X |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Medicine |
| spelling | doaj-art-b7182c57ff374f4abc31734ebc7e3eee2025-08-20T02:40:28ZengFrontiers Media S.A.Frontiers in Medicine2296-858X2025-07-011210.3389/fmed.2025.15518941551894A state-of-the-art review of diffusion model applications for microscopic image and micro-alike image analysisYan Liu0Tao Jiang1Rui Li2Lingling Yuan3Marcin Grzegorzek4Chen Li5Xiaoyan Li6College of Medicine and Biological Informaton Engineering, Northeastern University, Shenyang, ChinaCollege of Intelligent Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu, ChinaCollege of Medicine and Biological Informaton Engineering, Northeastern University, Shenyang, ChinaCollege of Medicine and Biological Informaton Engineering, Northeastern University, Shenyang, ChinaUniversity of Lübeck, Lübeck, GermanyCollege of Medicine and Biological Informaton Engineering, Northeastern University, Shenyang, ChinaHistopathology Department, Liaoning Cancer Hospital, Shenyang, ChinaDiffusion models, a class of deep learning models based on probabilistic generative processes, progressively transform data into noise and then reconstruct the original data through an inverse process. Recently, diffusion models have gained attention in microscopic image analysis for their ability to process complex data, extract valuable information, and enhance image quality. This review provides an overview of diffusion models in microscopic images and micro-alike images, focusing on three commonly used models: DDPM, DDIM, and SDEs. We explore their applications in image generation, segmentation, denoising, classification, reconstruction and super-resolution. It shows their notable advantages, particularly in image generation and segmentation. Through simulating the imaging process of biological samples under the microscope, diffusion model can generate high-quality synthetic microscopic images. The generated images serve as a powerful tool for data augmentation when training deep learning models. Diffusion model also excels in microscopic image segmentation. It enables to accurately segment different cellular regions and tissue structures by simulating the interactions between pixels in an image. The review includes 31 papers, with 13 on image generation, nine on segmentation, and the remainder on other applications. We also discuss the strengths, limitations, and future directions for diffusion models in biomedical image processing.https://www.frontiersin.org/articles/10.3389/fmed.2025.1551894/fullmicroscopic imagemicro-alike imagediffusion modelimage generationimage segmentationimage analysis |
| spellingShingle | Yan Liu Tao Jiang Rui Li Lingling Yuan Marcin Grzegorzek Chen Li Xiaoyan Li A state-of-the-art review of diffusion model applications for microscopic image and micro-alike image analysis Frontiers in Medicine microscopic image micro-alike image diffusion model image generation image segmentation image analysis |
| title | A state-of-the-art review of diffusion model applications for microscopic image and micro-alike image analysis |
| title_full | A state-of-the-art review of diffusion model applications for microscopic image and micro-alike image analysis |
| title_fullStr | A state-of-the-art review of diffusion model applications for microscopic image and micro-alike image analysis |
| title_full_unstemmed | A state-of-the-art review of diffusion model applications for microscopic image and micro-alike image analysis |
| title_short | A state-of-the-art review of diffusion model applications for microscopic image and micro-alike image analysis |
| title_sort | state of the art review of diffusion model applications for microscopic image and micro alike image analysis |
| topic | microscopic image micro-alike image diffusion model image generation image segmentation image analysis |
| url | https://www.frontiersin.org/articles/10.3389/fmed.2025.1551894/full |
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