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Nuclei segmentation and classification is a significant process in pathology image analysis.
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Doan, T.N.N., Kim, K., Song, B., Kwak, J.T.: Gradmix for nuclei segmentation and classification in imbalanced pathology image datasets. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2022: 25th International Conference, Singapore, September 18–22, 2022, Proceedings, Part II. pp. 171–180. Springer (2022)
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Dhariwal, P., Nichol, A.: Diffusion models beat gans on image synthesis. Advances in Neural Information Processing Systems 34
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Gong, X., Chen, S., Zhang, B., Doermann, D.: Style consistent image generation for nuclei instance segmentation. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. pp. 3994–4003 (2021)
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Nichol, A.Q., Dhariwal, P.: Improved denoising diffusion probabilistic models. In: International Conference on Machine Learning. pp. 8162–8171. PMLR (2021)
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Lin, Y., Wang, Z., Cheng, K.T., Chen, H.: InsMix: Towards realistic generative data augmentation for nuclei instance segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer (2022)
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Wolleb, J., Bieder, F., Sandkühler, R., Cattin, P.C.: Diffusion models for medical anomaly detection. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2022: 25th International Conference, Singapore, September 18–22, 2022, Proceedings, Part VIII. pp. 35–45. Springer (2022)
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