Lightweight Denoising Diffusion Implicit Model for Medical Segmentation

Automatic medical segmentation is crucial for assisting doctors in identifying disease regions effectively. As a state-of-the-art (SOTA) approach, generative AI models, particularly diffusion models, have surpassed GANs in generating high-quality images for tasks like segmentation. However, most diffusion-based architectures rely on U-Net designs with multiple residual blocks and convolutional layers, resulting in high computational costs and limited applicability on general-purpose devices. To solve this issue, we propose an enhanced denoising diffusion implicit model (DDIM) that incorporates lightweight depthwise convolution layers within residual networks and self-attention layers. This approach significantly reduces computational overhead while maintaining segmentation performance. We evaluated the proposed DDIM on two distinct medical imaging datasets: X-ray and skin lesion and polyp segmentation. Experimental results demonstrate that our model achieves, with reduced resource requirements, accuracy comparable to standard DDIMs in both visual representation and region-based scoring. The proposed lightweight DDIM offers a promising solution for medical segmentation tasks, enabling easier implementation on general-purpose devices without the need for expensive high-performance computing resources.