[18] Daniel M Ziegler, Nisan Stiennon, Jeffrey Wu, Tom B Brown, Alec Radford, Dario Amodei, Paul Christiano, and Geoffrey Irving. Fine-tuning language models from human preferences. arXiv preprint arXiv:1909.08593, 2019.
[19] Nisan Stiennon, Long Ouyang, Jeffrey Wu, Daniel Ziegler, Ryan Lowe, Chelsea Voss, Alec Radford, Dario Amodei, and Paul F Christiano. Learning to summarize with human feedback. Advances in Neural Information Processing Systems, 33:3008-3021, 2020.
[20] Kimin Lee, Hao Liu, Moonkyung Ryu, Olivia Watkins, Yuqing Du, Craig Boutilier, Pieter Abbeel, Mohammad Ghavamzadeh, and Shixiang Shane Gu. Aligning text-to-image models using human feedback. arXiv preprint arXiv:2302.12192, 2023.
[21] Mehdi SM Sajjadi, Olivier Bachem, Mario Lucic, Olivier Bousquet, and Sylvain Gelly. Assessing generative models via precision and recall. Advances in neural information processing systems, 31, 2018.
[22] Ahmed Alaa, Boris Van Breugel, Evgeny S Saveliev, and Mihaela van der Schaar. How faithful is your synthetic data? sample-level metrics for evaluating and auditing generative models. In International Conference on Machine Learning, pages 290-306. PMLR, 2022.
[23] Prafulla Dhariwal and Alexander Nichol. Diffusion models beat gans on image synthesis. Advances in Neural Information Processing Systems, 34:8780-8794, 2021.
[24] Diederik P Kingma and Max Welling. Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114, 2013.
[25] Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. Generative adversarial networks. Communications of the ACM, 63(11):139–144, 2020.
[26] Gustav Müller-Franzes, Jan Moritz Niehues, Firas Khader, Soroosh Tayebi Arasteh, Christoph Haarburger, Christiane Kuhl, Tianci Wang, Tianyu Han, Sven Nebelung, Jakob Nikolas Kather, et al. Diffusion probabilistic models beat gans on medical images. arXiv preprint arXiv:2212.07501, 2022.
[27] Walter HL Pinaya, Petru-Daniel Tudosiu, Jessica Dafflon, Pedro F Da Costa, Virginia Fernandez, Parashkev Nachev, Sebastien Ourselin, and M Jorge Cardoso. Brain imaging generation with latent diffusion models. In Deep Generative Models: Second MICCAI Workshop, DGM4MICCAI 2022, Held in Conjunction with MICCAI 2022, Singapore, September 22, 2022, Proceedings, pages 117-126. Springer, 2022.
[28] Firas Khader, Gustav Mueller-Franzes, Soroosh Tayebi Arasteh, Tianyu Han, Christoph Haarburger, Maximilian Schulze-Hagen, Philipp Schad, Sandy Engelhardt, Bettina Baessler, Sebastian Foersch, et al. Medical diffusion-denoising diffusion probabilistic models for 3d medical image generation. arXiv preprint arXiv:2211.03364, 2022.
[29] Amirhossein Kazerouni, Ehsan Khodapanah Aghdam, Moein Heidari, Reza Azad, Mohsen Fayyaz, Ilker Hacihaliloglu, and Dorit Merhof. Diffusion models for medical image analysis: A comprehensive survey. arXiv preprint arXiv:2211.07804, 2022.
[30] Tonghe Wang, Yang Lei, Zhen Tian, Xue Dong, Yingzi Liu, Xiaojun Jiang, Walter J Curran, Tian Liu, Hui-Kuo Shu, and Xiaofeng Yang. Deep learning-based image quality improvement for low-dose computed tomography simulation in radiation therapy. Journal of Medical Imaging, 6(4):043504-043504, 2019.
[31] Ilona Urbaniak and Marcin Wolter. Quality assessment of compressed and resized medical images based on pattern recognition using a convolutional neural network. Communications in Nonlinear Science and Numerical Simulation, 95:105582, 2021.
[32] Daniel Lévy and Arzav Jain. Breast mass classification from mammograms using deep convolutional neural networks. arXiv preprint arXiv:1612.00542, 2016.
[33] Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter. Gans trained by a two time-scale update rule converge to a local nash equilibrium. Advances in neural information processing systems, 30, 2017.
[34] Harry Nyquist. Certain factors affecting telegraph speed. Transactions of the American Institute of Electrical Engineers, 43:412-422, 1924.
[35] Stefan Winkler and Praveen Mohandas. The evolution of video quality measurement: From psnr to hybrid metrics. IEEE transactions on Broadcasting, 54(3):660-668, 2008.