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Image synthesis approaches, e.g., generative adversarial networks, have been popular as a form of data augmentation in medical image analysis tasks.
1902
Earlier work this paper cites.
Song, J., Meng, C., Ermon, S.: Denoising diffusion implicit models. CoRR abs/2010.02502
2010
Earlier work this paper cites.
Isola, P., Zhu, J.Y., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) pp. 5967–5976 (2016)
2016
Earlier work this paper cites.
Goodfellow, I.J.: NIPS 2016 tutorial: Generative adversarial networks. CoRR abs/1701.00160
2017
Earlier work this paper cites.
Wang, T.C., Liu, M.Y., Zhu, J.Y., Tao, A., Kautz, J., Catanzaro, B.: High-resolution image synthesis and semantic manipulation with conditional gans. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition pp. 8798–8807 (2017)
2017
Earlier work this paper cites.
Huang, X., Liu, M.Y., Belongie, S.J., Kautz, J.: Multimodal unsupervised image-to-image translation. In: European Conference on Computer Vision (2018)
2018
Earlier work this paper cites.
Karras, T., Laine, S., Aila, T.: A style-based generator architecture for generative adversarial networks. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) pp. 4396–4405 (2018)
2018
Cited alongside, same era.
Rai, H., Shukla, N.: Unpaired image-to-image translation using cycle-consistent adversarial networks (2018)
2018
Cited alongside, same era.
Shin, H.C., Tenenholtz, N.A., Rogers, J.K., Schwarz, C.G., Senjem, M.L., Gunter, J.L., Andriole, K.P., Michalski, M.H.: Medical image synthesis for data augmentation and anonymization using generative adversarial networks. In: SASHIMI@MICCAI (2018)
2018
Cited alongside, same era.
Shin, Y., Qadir, H.A., Balasingham, I.: Abnormal colon polyp image synthesis using conditional adversarial networks for improved detection performance. IEEE Access 6
2018
Cited alongside, same era.
Rombach, R., Blattmann, A., Lorenz, D., Esser, P., Ommer, B.: High-resolution image synthesis with latent diffusion models (2021)
2021
Later among the works it cites.
Hu, Q., Xiao, J., Chen, Y., Sun, S., Chen, J.N., Yuille, A., Zhou, Z.: Synthetic tumors make ai segment tumors better. NeurIPS Workshop on Medical Imaging meets NeurIPS (2022)
2022
Later among the works it cites.
Lyu, F., Ye, M., Carlsen, J.F., Erleben, K., Darkner, S., Yuen, P.C.: Pseudo-label guided image synthesis for semi-supervised covid-19 pneumonia infection segmentation. IEEE Transactions on Medical Imaging (2022)
2022
Later among the works it cites.
Hu, Q., Chen, Y., Xiao, J., Sun, S., Chen, J., Yuille, A.L., Zhou, Z.: Label-free liver tumor segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 7422–7432 (2023)
2023
Closest in time.
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Tschandl, P., Rosendahl, C., Kittler, H.: The ham10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions. Scientific data 5
2018
Cited alongside, same era.
Xu, Z., Wang, X., Shin, H.C., Yang, D., Roth, H.R., Milletarì, F., Zhang, L., Xu, D.: Correlation via synthesis: End-to-end image generation and radiogenomic learning based on generative adversarial network. In: International Conference on Medical Imaging with Deep Learning (2020)
2020
Cited alongside, same era.
Li, B., Chou, Y.C., Sun, S., Qiao, H., Yuille, A., Zhou, Z.: Early detection and localization of pancreatic cancer by label-free tumor synthesis. MICCAI Workshop on Big Task Small Data, 1001-AI (2023)
2023
Closest in time.