用神经元胞自动机提升皮肤病变分割效率，参数少60倍仍保持高精度。
MedSegDiffNCA: Diffusion Models With Neural Cellular Automata for Skin Lesion Segmentation
- 用多层级神经元胞自动机逐步优化噪声图像，替代传统UNet结构。
- 在皮肤病分割任务中达到87.84%的Dice分数，参数量仅为原模型的1/60至1/110。
- 适合资源受限的医疗场景，尤其对算力不足的临床环境有实用价值。
去噪扩散模型（DDMs）广泛用于高质量图像生成和医学图像分割，但通常依赖于基于UNet的架构，导致高分辨率图像下计算开销大。本文提出三种基于神经元胞自动机（NCA）的改进方法：首先，多层级NCA框架（Multi-MedSegDiffNCA）通过低层模型生成的粗略噪声估计进行逐级细化；其次，CBAM-MedSegDiffNCA引入通道与空间注意力机制提升分割性能；第三，多注意力融合模型（MultiCBAM-MedSegDiffNCA）结合上述方法并引入新提出的RGB通道损失以提供语义引导。在皮肤病变分割任务上的评估表明，该方法在达到87.84% Dice分数的同时，参数量减少60至110倍，为低资源医疗环境提供了更高效的解决方案。
原文摘要 · Abstract (English)
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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