MedSegDiff-V2: Diffusion-Based Medical Image Segmentation with Transformer
Document Type
Conference Proceeding
Publication Title
Proceedings of the AAAI Conference on Artificial Intelligence
Abstract
The Diffusion Probabilistic Model (DPM) has recently gained popularity in the field of computer vision, thanks to its image generation applications, such as Imagen, Latent Diffusion Models, and Stable Diffusion, which have demonstrated impressive capabilities and sparked much discussion within the community. Recent investigations have further unveiled the utility of DPM in the domain of medical image analysis, as underscored by the commendable performance exhibited by the medical image segmentation model across various tasks. Although these models were originally underpinned by a UNet architecture, there exists a potential avenue for enhancing their performance through the integration of vision transformer mechanisms. However, we discovered that simply combining these two models resulted in subpar performance. To effectively integrate these two cutting-edge techniques for the Medical image segmentation, we propose a novel Transformer-based Diffusion framework, called MedSegDiffV2. We verify its effectiveness on 20 medical image segmentation tasks with different image modalities. Through comprehensive evaluation, our approach demonstrates superiority over prior state-of-the-art (SOTA) methodologies.
First Page
6030
Last Page
6038
DOI
10.1609/aaai.v38i6.28418
Publication Date
3-25-2024
Recommended Citation
J. Wu et al., "MedSegDiff-V2: Diffusion-Based Medical Image Segmentation with Transformer," Proceedings of the AAAI Conference on Artificial Intelligence, vol. 38, no. 6, pp. 6030 - 6038, Mar 2024.
The definitive version is available at https://doi.org/10.1609/aaai.v38i6.28418