Importance of Aligning Training Strategy with Evaluation for Diffusion Models in 3D Multiclass Segmentation

Author:

Fu YunguanORCID,Li YiwenORCID,Saeed Shaheer U.ORCID,Clarkson Matthew J.ORCID,Hu YipengORCID

Publisher

Springer Nature Switzerland

Reference28 articles.

1. Ali, H., Murad, S., Shah, Z.: Spot the fake lungs: generating synthetic medical images using neural diffusion models. In: Longo, L., O’Reilly, R. (eds.) Artificial Intelligence and Cognitive Science. Communications in Computer and Information Science, vol. 1662. pp. 32–39. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-26438-2_3

2. Amit, T., Nachmani, E., Shaharbany, T., Wolf, L.: SegDiff: image segmentation with diffusion probabilistic models. arXiv preprint: arXiv:2112.00390 (2021)

3. Baranchuk, D., Rubachev, I., Voynov, A., Khrulkov, V., Babenko, A.: Label-efficient semantic segmentation with diffusion models. arXiv preprint: arXiv:2112.03126 (2021)

4. Chen, T., Li, L., Saxena, S., Hinton, G., Fleet, D.J.: A generalist framework for panoptic segmentation of images and videos. arXiv preprint: arXiv:2210.06366 (2022)

5. Dhariwal, P., Nichol, A.: Diffusion models beat GANs on image synthesis. In: Advances in Neural Information Processing Systems, vol. 34, pp. 8780–8794 (2021)

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