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Despite continued advancement in recent years, deep neural networks still rely on large amounts of training data to avoid overfitting.
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Hu, D., Tao, Y.K., Oguz, I.: Unsupervised denoising of retinal oct with diffusion probabilistic model. In: Medical Imaging 2022: Image Processing. vol. 12032, pp. 25–34. SPIE (2022)
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Kumar, A.J.S., Chong, R.S., Crowston, J.G., Chua, J., Bujor, I., Husain, R., Vithana, E.N., Girard, M.J., Ting, D.S., Cheng, C.Y., et al.: Evaluation of generative adversarial networks for high-resolution synthetic image generation of circumpapillary optical coherence tomography images for glaucoma. JAMA ophthalmology 140
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Sanchez, P., Kascenas, A., Liu, X., O’Neil, A.Q., Tsaftaris, S.A.: What is healthy? generative counterfactual diffusion for lesion localization. In: MICCAI Workshop on Deep Generative Models. pp. 34–44. Springer (2022)
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Pinaya, W.H., Tudosiu, P.D., Dafflon, J., Da Costa, P.F., Fernandez, V., Nachev, P., Ourselin, S., Cardoso, M.J.: Brain imaging generation with latent diffusion models. In: MICCAI Workshop on Deep Generative Models. pp. 117–126. Springer (2022)
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Rombach, R., Blattmann, A., Lorenz, D., Esser, P., Ommer, B.: High-resolution image synthesis with latent diffusion models. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 10684–10695 (2022)
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Wyatt, J., Leach, A., Schmon, S.M., Willcocks, C.G.: Anoddpm: Anomaly detection with denoising diffusion probabilistic models using simplex noise. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 650–656 (2022)
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Large-scale Artificial Intelligence Open Network. https://laion.ai , accessed: 2023-01-11
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