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The universal model emerges as a promising trend for medical image segmentation, paving up the way to build medical imaging large model (MILM).
Landman, B., Xu, Z., Igelsias, J., Styner, M., Langerak, T., Klein, A.: Miccai multi-atlas labeling beyond the cranial vault–workshop and challenge. In: Proc. MICCAI Multi-Atlas Labeling Beyond Cranial Vault—Workshop Challenge. vol. 5, p. 12 (2015)
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2019
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Conneau, A., Lample, G.: Cross-lingual language model pretraining. Advances in neural information processing systems 32
2019
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Fang, X., Yan, P.: Multi-organ segmentation over partially labeled datasets with multi-scale feature abstraction. IEEE Transactions on Medical Imaging 39
2020
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Liu, Q., Dou, Q., Yu, L., Heng, P.A.: Ms-net: multi-site network for improving prostate segmentation with heterogeneous mri data. IEEE transactions on medical imaging 39
2020
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2021
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2021
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2021
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Heller, N., Isensee, F., Maier-Hein, K.H., Hou, X., Xie, C., Li, F., Nan, Y., Mu, G., Lin, Z., Han, M., et al.: The state of the art in kidney and kidney tumor segmentation in contrast-enhanced ct imaging: Results of the kits19 challenge. Medical image analysis 67
2021
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Isensee, F., Jaeger, P.F., Kohl, S.A., Petersen, J., Maier-Hein, K.H.: nnu-net: a self-configuring method for deep learning-based biomedical image segmentation. Nature methods 18
2021
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Sekuboyina, A., Husseini, M.E., Bayat, A., Löffler, M., Liebl, H., Li, H., Tetteh, G., Kukačka, J., Payer, C., Štern, D., et al.: Verse: A vertebrae labelling and segmentation benchmark for multi-detector ct images. Medical image analysis 73
2021
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Shapey, J., Kujawa, A., Dorent, R., Wang, G., Bisdas, S., Dimitriadis, A., Grishchuck, D., Paddick, I., Kitchen, N., Bradford, R., et al.: Segmentation of vestibular schwannoma from magnetic resonance imaging: an open annotated dataset and baseline algorithm. The Cancer Imaging Archive (2021)
2021
Cited alongside, same era.
Shi, G., Xiao, L., Chen, Y., Zhou, S.K.: Marginal loss and exclusion loss for partially supervised multi-organ segmentation. Medical Image Analysis 70
2021
Cited alongside, same era.
Xie, Y., Zhang, J., Shen, C., Xia, Y.: Cotr: Efficiently bridging cnn and transformer for 3d medical image segmentation. In: Medical Image Computing and Computer Assisted Intervention. pp. 171–180. Springer (2021)
2021
2022
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Tang, Y., Yang, D., Li, W., Roth, H.R., Landman, B., Xu, D., Nath, V., Hatamizadeh, A.: Self-supervised pre-training of swin transformers for 3d medical image analysis. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 20730–20740 (2022)
2022
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Wang, W., Xie, E., Li, X., Fan, D.P., Song, K., Liang, D., Lu, T., Luo, P., Shao, L.: Pvt v2: Improved baselines with pyramid vision transformer. Computational Visual Media 8
2022
Later among the works it cites.
Wang, Z., Zhang, Z., Lee, C.Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 139–149 (2022)
2022
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Cited alongside, same era.
Zhang, J., Xie, Y., Xia, Y., Shen, C.: DoDnet: Learning to segment multi-organ and tumors from multiple partially labeled datasets. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 1195–1204 (2021)
2021
Cited alongside, same era.
2021
Cited alongside, same era.
Zhou, Z., Sodha, V., Pang, J., Gotway, M.B., Liang, J.: Models genesis. Medical image analysis 67
2021
Cited alongside, same era.
Gatidis, S., Hepp, T., Früh, M., La Fougère, C., Nikolaou, K., Pfannenberg, C., Schölkopf, B., Küstner, T., Cyran, C., Rubin, D.: A whole-body fdg-pet/ct dataset with manually annotated tumor lesions. Scientific Data 9
2022
Cited alongside, same era.
Hatamizadeh, A., Tang, Y., Nath, V., Yang, D., Myronenko, A., Landman, B., Roth, H.R., Xu, D.: Unetr: Transformers for 3d medical image segmentation. In: Proceedings of the IEEE/CVF winter conference on applications of computer vision. pp. 574–584 (2022)
2022
Cited alongside, same era.
Jiang, J., Tyagi, N., Tringale, K., Crane, C., Veeraraghavan, H.: Self-supervised 3d anatomy segmentation using self-distilled masked image transformer (smit). In: Medical Image Computing and Computer Assisted Intervention. pp. 556–566. Springer (2022)
2022
Cited alongside, same era.
Later among the works it cites.
Wu, H., Pang, S., Sowmya, A.: Tgnet: A task-guided network architecture for multi-organ and tumour segmentation from partially labelled datasets. In: International Symposium on Biomedical Imaging. pp. 1–5. IEEE (2022)
2022
Later among the works it cites.
Xie, Y., Zhang, J., Xia, Y., Wu, Q.: Unimiss: Universal medical self-supervised learning via breaking dimensionality barrier. In: European Conference on Computer Vision. pp. 558–575. Springer (2022)
2022
Later among the works it cites.
Ye, Y., Zhang, J., Chen, Z., Xia, Y.: DeSD: Self-supervised learning with deep self-distillation for 3d medical image segmentation. In: Medical Image Computing and Computer Assisted Intervention. pp. 545–555. Springer (2022)
2022
Later among the works it cites.
He, Y., Yang, G., Ge, R., Chen, Y., Coatrieux, J.L., Wang, B., Li, S.: Geometric visual similarity learning in 3d medical image self-supervised pre-training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (2023)
2023
Closest in time.
Lee, H.H., Bao, S., Huo, Y., Landman, B.A.: 3d UX-net: A large kernel volumetric convnet modernizing hierarchical transformer for medical image segmentation. In: The Eleventh International Conference on Learning Representations (2023)
2023
Closest in time.