MedSegDiffNCA: Diffusion Models With Neural Cellular Automata for Skin Lesion Segmentation

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, CBAMMedSegDiffNCA incorporates channel and spatial attention for improved segmentation. Second, Multi-MedSegDiffNCA uses a multilevel NCA framework to refine rough noise estimates generated by lower-level NCA models. Third, MultiCBAMMedSegDiffNCA 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 a dice score of 87.84% while using 60-110 times fewer parameters and 5 times faster training, offering an efficient solution for low-resource medical settings.