MPromer: A Unified Diffusion-Based Framework for Scalable and Generalizable Multi-Modal Medical Image Segmentation
Ghallabi, Wafa Al Dudhane, Akshay Zamir, Syed Waqas Khan, Salman Khan, Fahad Shahbaz
Ghallabi, Wafa Al
Dudhane, Akshay
Zamir, Syed Waqas
Khan, Salman
Khan, Fahad Shahbaz
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Computer Vision
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Conference proceeding
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Abstract
Multi-modal medical image analysis is essential for comprehensive diagnostics; however, existing segmentation models often struggle to generalize across diverse imaging modalities such as MRI, CT, fundus imaging, and colonoscopy. While recent diffusion-based approaches have shown promising results, they typically rely on task-specific training, which limits their scalability and imposes significant computational demands. To address these limitations, we propose MPromer, a robust and adaptable segmentation framework that incorporates multi-scale implicit prompting within a diffusion-based architecture. In contrast to traditional prompt-driven methods, MPromer adapts automatically to various imaging modalities without requiring manually designed prompts or retraining for individual tasks. By integrating prompt-conditioned diffusion processes into an encoder-decoder structure, the model achieves consistent and effective segmentation across a wide range of medical domains. We evaluate MPromer on six benchmark datasets, demonstrating state-of-the-art performance with strong generalization capabilities. In addition to improved segmentation accuracy, MPromer enhances computational efficiency and extends naturally to multi-label segmentation tasks, making it well-suited for complex clinical applications. The framework provides a scalable and efficient solution that minimizes the need for fine-tuning, which is particularly beneficial in resource-constrained medical environments. Our code and models are available at https://github.com/wafaAlghallabi/MPromer.
Citation
W.A. Ghallabi, A. Dudhane, S.W. Zamir, S. Khan, F.S. Khan, "MPromer: A Unified Diffusion-Based Framework for Scalable and Generalizable Multi-Modal Medical Image Segmentation," 2026, pp. 930-938.
Source
2025 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)
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2025 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)
Keywords
46 Information and Computing Sciences, 4603 Computer Vision and Multimedia Computation
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2025 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)
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IEEE