Keywords: AI Diffusion Models, Segmentation
Motivation: Glioblastoma, the most prevalent and aggressive adult brain tumor, presents a therapeutic and monitoring challenge due to its diverse morphology and composition.
Goal(s): This study explores the efficacy of utilizing denoising diffusion models with the widely adopted U-Net architecture for enhanced segmentation performance.
Approach: The proposed framework notably improves the segmentation of the tumor, especially the core. This enhancement facilitates an advanced understanding of complex cases and potentially impacts specialist interventions.
Results: Our findings present promising results for further research into more intricate glioblastoma cases, thereby aiding in developing sophisticated, targeted treatment strategies for this disease.
Impact: This study advances U-Net architecture by integrating denoising diffusion models and specialized loss functions, elevating the precision of low-resolution brain tumor segmentation, with an emphasis on balancing improved accuracy against uncertain predictions.
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