Stable Diffusion Segmentation for Biomedical Images with Single-step Reverse Process

Autor:	Lin, Tianyu, Chen, Zhiguang, Yan, Zhonghao, Yu, Weijiang, Zheng, Fudan
Rok vydání:	2024
Předmět:	Computer Science - Computer Vision and Pattern Recognition Computer Science - Artificial Intelligence Electrical Engineering and Systems Science - Image and Video Processing
Druh dokumentu:	Working Paper
Popis:	Diffusion models have demonstrated their effectiveness across various generative tasks. However, when applied to medical image segmentation, these models encounter several challenges, including significant resource and time requirements. They also necessitate a multi-step reverse process and multiple samples to produce reliable predictions. To address these challenges, we introduce the first latent diffusion segmentation model, named SDSeg, built upon stable diffusion (SD). SDSeg incorporates a straightforward latent estimation strategy to facilitate a single-step reverse process and utilizes latent fusion concatenation to remove the necessity for multiple samples. Extensive experiments indicate that SDSeg surpasses existing state-of-the-art methods on five benchmark datasets featuring diverse imaging modalities. Remarkably, SDSeg is capable of generating stable predictions with a solitary reverse step and sample, epitomizing the model's stability as implied by its name. The code is available at https://github.com/lin-tianyu/Stable-Diffusion-Seg Comment: Accepted at MICCAI 2024. Code and citation info see https://github.com/lin-tianyu/Stable-Diffusion-Seg
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2406.18361 Zobrazit plný text záznamu View this record from Arxiv