Accelerating Diffusion Models via Pre-segmentation Diffusion Sampling for Medical Image Segmentation

Kingma D P, Welling M. Auto-encoding variational bayes[J]. arXiv preprint arXiv:1312.6114, 2013

Original

2013

Earlier work this paper cites.

Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2014. Generative adversarial nets. In Advances in Neural Information Processing Systems, Vol. 27. 139–144

2014

Earlier work this paper cites.

Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., & Ganguli, S. (2015, June). Deep unsupervised learning using nonequilibrium thermodynamics. In International Conference on Machine Learning (pp. 2256-2265). PMLR

2015

Earlier work this paper cites.

Ronneberger O, Fischer P, Brox T. U-net: Convolutional networks for biomedical image segmentation[C]//International Conference on Medical image computing and computer-assisted intervention. Springer, Cham, 2015: 234-241

2015

Earlier work this paper cites.

Gal, Yarin, and Zoubin Ghahramani. ”Dropout as a bayesian approximation: Representing model uncertainty in deep learning.” international conference on machine learning. PMLR, 2016

2016

Earlier work this paper cites.

P. Suetens, “Fundamentals of medical imaging, 3rd edition,” 2017

2017

Earlier work this paper cites.

Oktay O, Schlemper J, Folgoc L L, et al. Attention u-net: Learning where to look for the pancreas[J]. arXiv preprint arXiv:1804.03999, 2018

Original

2018

Earlier work this paper cites.

Kohl, Simon, et al. ”A probabilistic u-net for segmentation of ambiguous images.” Advances in neural information processing systems 31 (2018)

2018

Earlier work this paper cites.

Li H, Jiang G, Zhang J, et al. Fully convolutional network ensembles for white matter hyperintensities segmentation in MR images[J]. NeuroImage, 2018, 183: 650-665

2018

Cited alongside, same era.

Kuijf H J, Biesbroek J M, De Bresser J, et al. Standardized assessment of automatic segmentation of white matter hyperintensities and results of the WMH segmentation challenge[J]. IEEE transactions on medical imaging, 2019, 38(11): 2556-2568

2019

Cited alongside, same era.

Zhou Z, Siddiquee M M R, Tajbakhsh N, et al. Unet++: Redesigning skip connections to exploit multiscale features in image segmentation[J]. IEEE transactions on medical imaging, 2019, 39(6): 1856-1867

2019

Cited alongside, same era.

Ho, J., Jain, A., & Abbeel, P. (2020). Denoising diffusion probabilistic models. Advances in Neural Information Processing Systems, 33, 6840-6851

2020

Cited alongside, same era.

Salimans T, Ho J. Progressive Distillation for Fast Sampling of Diffusion Models[C]//International Conference on Learning Representations. 2021

2021

Later among the works it cites.

Vahdat A, Kreis K, Kautz J. Score-based generative modeling in latent space[J]. Advances in Neural Information Processing Systems, 2021, 34: 11287-11302

2021

Later among the works it cites.

Yang, L., Zhang, Z., & Hong, S. (2022). Diffusion models: A comprehensive survey of methods and applications. arXiv preprint arXiv:2209.00796

Original

2022

Closest in time.

Croitoru, F. A., Hondru, V., Ionescu, R. T., & Shah, M. (2022). Diffusion models in vision: A survey. arXiv preprint arXiv:2209.04747

Original

2022

Closest in time.

Rombach R, Blattmann A, Lorenz D, et al. High-resolution image synthesis with latent diffusion models[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2022: 10684-10695