MedDiff-FM: A Diffusion-based Foundation Model for Versatile Medical Image Applications
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866908694552248320 |
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| author | Yu, Yongrui Gu, Yannian Zhang, Shaoting Zhang, Xiaofan |
| author_facet | Yu, Yongrui Gu, Yannian Zhang, Shaoting Zhang, Xiaofan |
| contents | Diffusion models have achieved significant success in both natural image and medical image domains, encompassing a wide range of applications. Previous investigations in medical images have often been constrained to specific anatomical regions, particular applications, and limited datasets, resulting in isolated diffusion models. This paper introduces a diffusion-based foundation model to address a diverse range of medical image tasks, namely MedDiff-FM. MedDiff-FM leverages 3D CT images from multiple publicly available datasets, covering anatomical regions from head to abdomen, to pre-train a diffusion foundation model, and explores the capabilities of the diffusion foundation model across a variety of application scenarios. The diffusion foundation model handles multi-level integrated image processing both at the image-level and patch-level, utilizes position embedding to establish multi-level spatial relationships, and leverages region classes and anatomical structures to capture certain anatomical regions. MedDiff-FM manages several downstream tasks seamlessly, including image denoising, anomaly detection, and image synthesis. MedDiff-FM is also capable of performing super-resolution, lesion generation, and lesion inpainting by rapidly fine-tuning the diffusion foundation model using ControlNet with task-specific conditions. The experimental results demonstrate the effectiveness of MedDiff-FM in addressing diverse downstream medical image tasks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_15432 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | MedDiff-FM: A Diffusion-based Foundation Model for Versatile Medical Image Applications Yu, Yongrui Gu, Yannian Zhang, Shaoting Zhang, Xiaofan Computer Vision and Pattern Recognition Diffusion models have achieved significant success in both natural image and medical image domains, encompassing a wide range of applications. Previous investigations in medical images have often been constrained to specific anatomical regions, particular applications, and limited datasets, resulting in isolated diffusion models. This paper introduces a diffusion-based foundation model to address a diverse range of medical image tasks, namely MedDiff-FM. MedDiff-FM leverages 3D CT images from multiple publicly available datasets, covering anatomical regions from head to abdomen, to pre-train a diffusion foundation model, and explores the capabilities of the diffusion foundation model across a variety of application scenarios. The diffusion foundation model handles multi-level integrated image processing both at the image-level and patch-level, utilizes position embedding to establish multi-level spatial relationships, and leverages region classes and anatomical structures to capture certain anatomical regions. MedDiff-FM manages several downstream tasks seamlessly, including image denoising, anomaly detection, and image synthesis. MedDiff-FM is also capable of performing super-resolution, lesion generation, and lesion inpainting by rapidly fine-tuning the diffusion foundation model using ControlNet with task-specific conditions. The experimental results demonstrate the effectiveness of MedDiff-FM in addressing diverse downstream medical image tasks. |
| title | MedDiff-FM: A Diffusion-based Foundation Model for Versatile Medical Image Applications |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2410.15432 |