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Medical image segmentation has been traditionally approached by training or fine-tuning the entire model to cater to any new modality or dataset.
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Valanarasu, J.M.J., Oza, P., Hacihaliloglu, I., Patel, V.M.: Medical transformer: Gated axial-attention for medical image segmentation. In: Medical Image Computing and Computer Assisted Intervention – MICCAI 2021. pp. 36–46. Springer International Publishing, Cham (2021)
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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: 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). pp. 1748–1758 (2022)
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Basak, H., Yin, Z.: Pseudo-label guided contrastive learning for semi-supervised medical image segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 19786–19797 (June 2023)
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Rahman, A., Valanarasu, J., Hacihaliloglu, I., Patel, V.M.: Ambiguous medical image segmentation using diffusion models. In: 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 11536–11546. IEEE Computer Society, Los Alamitos, CA, USA (jun 2023), https://doi.ieeecomputersociety.org/10.1109/CVPR52729.2023.01110
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Wu, J., Zhang, Y., Fu, R., Fang, H., Liu, Y., Wang, Z., Xu, Y., Jin, Y.: Medical sam adapter: Adapting segment anything model for medical image segmentation (2023)
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2024
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