MedSegDiffNCA: Diffusion Models With Neural Cellular Automata for Skin Lesion Segmentation
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866912177655382016 |
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| author | Mittal, Avni Kalkhof, John Mukhopadhyay, Anirban Bhavsar, Arnav |
| author_facet | Mittal, Avni Kalkhof, John Mukhopadhyay, Anirban Bhavsar, Arnav |
| contents | Denoising Diffusion Models (DDMs) are widely used for high-quality image generation and medical image segmentation but often rely on Unet-based architectures, leading to high computational overhead, especially with high-resolution images. This work proposes three NCA-based improvements for diffusion-based medical image segmentation. First, Multi-MedSegDiffNCA uses a multilevel NCA framework to refine rough noise estimates generated by lower level NCA models. Second, CBAM-MedSegDiffNCA incorporates channel and spatial attention for improved segmentation. Third, MultiCBAM-MedSegDiffNCA combines these methods with a new RGB channel loss for semantic guidance. Evaluations on Lesion segmentation show that MultiCBAM-MedSegDiffNCA matches Unet-based model performance with dice score of 87.84% while using 60-110 times fewer parameters, offering a more efficient solution for low resource medical settings. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_02447 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MedSegDiffNCA: Diffusion Models With Neural Cellular Automata for Skin Lesion Segmentation Mittal, Avni Kalkhof, John Mukhopadhyay, Anirban Bhavsar, Arnav Computer Vision and Pattern Recognition Machine Learning Image and Video Processing Denoising Diffusion Models (DDMs) are widely used for high-quality image generation and medical image segmentation but often rely on Unet-based architectures, leading to high computational overhead, especially with high-resolution images. This work proposes three NCA-based improvements for diffusion-based medical image segmentation. First, Multi-MedSegDiffNCA uses a multilevel NCA framework to refine rough noise estimates generated by lower level NCA models. Second, CBAM-MedSegDiffNCA incorporates channel and spatial attention for improved segmentation. Third, MultiCBAM-MedSegDiffNCA combines these methods with a new RGB channel loss for semantic guidance. Evaluations on Lesion segmentation show that MultiCBAM-MedSegDiffNCA matches Unet-based model performance with dice score of 87.84% while using 60-110 times fewer parameters, offering a more efficient solution for low resource medical settings. |
| title | MedSegDiffNCA: Diffusion Models With Neural Cellular Automata for Skin Lesion Segmentation |
| topic | Computer Vision and Pattern Recognition Machine Learning Image and Video Processing |
| url | https://arxiv.org/abs/2501.02447 |