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Generative latent diffusion models have been established as state-of-the-art in data generation.
Schroff, F., Kalenichenko, D., Philbin, J.: Facenet: A unified embedding for face recognition and clustering. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (June 2015)
2015
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Bien, N., Rajpurkar, P., Ball, R.L., Irvin, J., Park, A., Jones, E., Bereket, M., Patel, B.N., Yeom, K.W., Shpanskaya, K., Halabi, S., Zucker, E., Fanton, G., Amanatullah, D.F., Beaulieu, C.F., Riley, G.M., Stewart, R.J., Blankenberg, F.G., Larson, D.B., Jones, R.H., Langlotz, C.P., Ng, A.Y., Lungren, M.P.: Deep-learning-assisted diagnosis for knee magnetic resonance imaging: Development and retrospective validation of mrnet. PLOS Medicine 15
2018
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Engelhardt, S., Sharan, L., Karck, M., Simone, R.D., Wolf, I.: Cross-domain conditional generative adversarial networks for stereoscopic hyperrealism in surgical training. In: Shen, D., Liu, T., Peters, T.M., Staib, L.H., Essert, C., Zhou, S., Yap, P.T., Khan, A. (eds.) Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. pp. 155–163. Springer International Publishing, Cham (2019)
2019
Earlier work this paper cites.
Pfeiffer, M., Funke, I., Robu, M.R., Bodenstedt, S., Strenger, L., Engelhardt, S., Roß, T., Clarkson, M.J., Gurusamy, K., Davidson, B.R., Maier-Hein, L., Riediger, C., Welsch, T., Weitz, J., Speidel, S.: Generating large labeled data sets for laparoscopic image processing tasks using unpaired image-to-image translation. In: Shen, D., Liu, T., Peters, T.M., Staib, L.H., Essert, C., Zhou, S., Yap, P.T., Khan, A. (eds.) Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. pp. 119–127. Springer International Publishing, Cham (2019)
2019
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Yi, X., Walia, E., Babyn, P.: Generative adversarial network in medical imaging: A review. Medical Image Analysis 58
2019
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Ho, J., Jain, A., Abbeel, P.: Denoising Diffusion Probabilistic Models. In: Larochelle, H., Ranzato, M., Hadsell, R., Balcan, M.F., Lin, H. (eds.) Advances in Neural Information Processing Systems. vol. 33, pp. 6840–6851. Curran Associates, Inc. (2020)
2020
Earlier work this paper cites.
Dorjsembe, Z., Odonchimed, S., Xiao, F.: Three-dimensional medical image synthesis with denoising diffusion probabilistic models. In: Medical Imaging with Deep Learning (2022)
2022
Earlier work this paper cites.
Pinaya, W.H.L., 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: Deep Generative Models. pp. 117–126. Springer Nature Switzerland, Cham (2022)
2022
Cited alongside, same era.
Rombach, R., Blattmann, A., Lorenz, D., Esser, P., Ommer, B.: High-Resolution Image Synthesis with Latent Diffusion Models. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 10674–10685 (2022). https://doi.org/10.1109/CVPR52688.2022.01042
2022
Cited alongside, same era.
Wolleb, J., Bieder, F., Sandkühler, R., Cattin, P.C.: Diffusion models for medical anomaly detection. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds.) Medical Image Computing and Computer Assisted Intervention – MICCAI 2022. pp. 35–45. Springer Nature Switzerland, Cham (2022)
2022
Cited alongside, same era.
Güngör, A., Dar, S.U., Şaban Öztürk, Korkmaz, Y., Bedel, H.A., Elmas, G., Ozbey, M., Çukur, T.: Adaptive diffusion priors for accelerated mri reconstruction. Medical Image Analysis p. 102872 (2023). https://doi.org/https://doi.org/10.1016/j.media.2023.102872
2023
Closest in time.
Kazerouni, A., Aghdam, E.K., Heidari, M., Azad, R., Fayyaz, M., Hacihaliloglu, I., Merhof, D.: Diffusion models in medical imaging: A comprehensive survey. Medical Image Analysis 88
2023
Closest in time.
Khader, F., Müller-Franzes, G., Tayebi Arasteh, S., Han, T., Haarburger, C., Schulze-Hagen, M., Schad, P., Engelhardt, S., Baeßler, B., Foersch, S., Stegmaier, J., Kuhl, C., Nebelung, S., Kather, J.N., Truhn, D.: Denoising diffusion probabilistic models for 3D medical image generation. Scientific Reports 13
2023
Closest in time.
Özbey, M., Dalmaz, O., Dar, S.U., Bedel, H.A., Özturk, c., Güngör, A., Çukur, T.: Unsupervised medical image translation with adversarial diffusion models. IEEE Transactions on Medical Imaging pp. 1–1 (2023). https://doi.org/10.1109/TMI.2023.3290149
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Wolleb, J., Sandkühler, R., Bieder, F., Valmaggia, P., Cattin, P.C.: Diffusion models for implicit image segmentation ensembles. In: Konukoglu, E., Menze, B., Venkataraman, A., Baumgartner, C., Dou, Q., Albarqouni, S. (eds.) Proceedings of The 5th International Conference on Medical Imaging with Deep Learning. Proceedings of Machine Learning Research, vol. 172, pp. 1336–1348. PMLR (06–08 Jul 2022)
2022
Cited alongside, same era.
Akbar, M.U., Wang, W., Eklund, A.: Beware of diffusion models for synthesizing medical images – a comparison with gans in terms of memorizing brain tumor images (2023)
2023
Cited alongside, same era.
Carlini, N., Hayes, J., Nasr, M., Jagielski, M., Sehwag, V., Tramèr, F., Balle, B., Ippolito, D., Wallace, E.: Extracting training data from diffusion models (2023)
2023
Cited alongside, same era.
2023
Closest in time.
Somepalli, G., Singla, V., Goldblum, M., Geiping, J., Goldstein, T.: Diffusion art or digital forgery? investigating data replication in diffusion models. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 6048–6058 (June 2023)
2023
Closest in time.
Somepalli, G., Singla, V., Goldblum, M., Geiping, J., Goldstein, T.: Understanding and mitigating copying in diffusion models (2023)
2023
Closest in time.