MedSegDiff-V2: Diffusion based Medical Image Segmentation with Transformer

By Junde Wu, Rao Fu, Huihui Fang, Yu Zhang, Yanwu Xu. AAAI Conference on Artificial Intelligence, 2023

353 citations recorded by the source corpus.

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 MedSegDiff-V2. 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. Code is released at https://github.com/KidsWithTokens/MedSegDiff.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/3f77a62ae888c3b816eabd354a6dd0fc6b9528ea

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Zhiling Yan
2. Lifang He
3. Lichao Sun

In Computer Vision and Pattern Recognition

1. Zhiling Yan
2. Yue Huang
3. Yuan Li
4. Kai Zhang
5. Zhengqing Yuan
6. Yixin Liu
7. Chujie Gao
8. Ruoxi Chen

Citations in the corpus

• Sora: A Review on Background, Technology, Limitations, and Opportunities of Large Vision Models (2024, methodology): “Additionally, models like MedSegDiff-V2 [161] and [162] leverage the power of transformers to segment medical images with unprecedented precision, enabling clinicians to pinpoint areas of interest across various imaging modalities with enhanced accuracy.”