TeNCA, a temporal neural cellular automata trained with adaptive losses at irregularly spaced time points, produces synthetic post-contrast breast MRI images that match ground truth more closely than U-Net and latent diffusion baselines.
MedSegDiffNCA: Diffusion Models With Neural Cellular Automata for Skin Lesion Segmentation
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abstract
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.
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Temporal Neural Cellular Automata: Application to modeling of contrast enhancement in breast MRI
TeNCA, a temporal neural cellular automata trained with adaptive losses at irregularly spaced time points, produces synthetic post-contrast breast MRI images that match ground truth more closely than U-Net and latent diffusion baselines.