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.
@article{arxiv.2501.02447,
title = {MedSegDiffNCA: Diffusion Models With Neural Cellular Automata for Skin Lesion Segmentation},
author = {Avni Mittal and John Kalkhof and Anirban Mukhopadhyay and Arnav Bhavsar},
journal= {arXiv preprint arXiv:2501.02447},
year = {2025}
}