BGDiffSeg: A Fast Diffusion Model for Skin Lesion Segmentation via Boundary Enhancement and Global Recognition Guidance

In the study of skin lesion segmentation, models based on convolution neural networks (CNN) and vision transformers (ViT) have been extensively explored but face challenges in capturing fine details near boundaries. The advent of Diffusion Probabilistic Model (DPM) offers significant promise for this task which demands precise boundary segmentation. In this study, we propose BGDiffSeg, a novel skin lesion segmentation model utilizing a wavelet-transform-based diffusion approach to speed up training and denoising, along with specially designed Diffusion Boundary Enhancement Module (DBEM) and Interactive Bidirectional Attention Module (IBAM) to enhance segmentation accuracy. DBEM enhances boundary features in the diffusion process by integrating extracted boundary information into the decoder. Concurrently, IBAM facilitates dynamic interactions between conditional and generated images at the feature level, thus enhancing the global recognition of target area boundaries. Comprehensive experiments on the ISIC 2016, ISIC 2017, and ISIC 2018 datasets demonstrate BGDiffSeg's superiority in precision and clarity under limited computational resources and inference time, outperforming existing state-of-the-art methods. Our code will be available at https://github.com/erlingzz/BGDiffSeg.