Diffusion semantic segmentation model: A generative model for medical image segmentation based on joint distribution
Our methodology enables the stabilization of the learned feature space by effectively capturing the latent feature distribution information. Experimental results demonstrate that our model considerably outperforms traditional discriminative segmentation methods across a variety of datasets from multiple modalities.
Authors
- Jayaram K. Udupa (ORCID: https://orcid.org/0000-0002-7361-2927)
- Drew A. Torigian (ORCID: https://orcid.org/0000-0001-8999-9735)
- Tiange Liu (ORCID: https://orcid.org/0000-0001-9835-8200)
- Yubing Tong
- Kaige Zhang (ORCID: https://orcid.org/0000-0003-0935-2734)
- Jinze Li (ORCID: https://orcid.org/0000-0001-6725-3318)
- Qibing Xiong (ORCID: https://orcid.org/0009-0001-6436-7739)
Institutions
- Yanshan University (CN)
- Jiangsu Police Officer College (CN)
- University of Pennsylvania (US)
- University of Science and Technology Beijing (CN)
Publication Details
- Journal
- Medical Physics
- Published
- 2025-06-08
- DOI
- https://doi.org/10.1002/mp.17928
- Citations
- 3
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 4.08
Funders
- National Natural Science Foundation of China
- China Postdoctoral Science Foundation
- Natural Science Foundation of Shandong Province