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Surgical image segmentation is highly challenging, primarily due to scarcity of annotated data.
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2021
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Lin, W., Hu, Y., Hao, L., Zhou, D., Yang, M., Fu, H., Chui, C., Liu, J.: Instrument-tissue interaction quintuple detection in surgery videos. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 399–409. Springer (2022)
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Nwoye, C.I., Yu, T., Gonzalez, C., Seeliger, B., Mascagni, P., Mutter, D., Marescaux, J., Padoy, N.: Rendezvous: Attention mechanisms for the recognition of surgical action triplets in endoscopic videos. Medical Image Analysis 78
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Wu, Q., Zhang, Y., Elbatel, M.: Self-prompting large vision models for few-shot medical image segmentation. In: MICCAI Workshop on Domain Adaptation and Representation Transfer. pp. 156–167. Springer (2023)
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Ouyang, C., Biffi, C., Chen, C., Kart, T., Qiu, H., Rueckert, D.: Self-supervised learning for few-shot medical image segmentation. IEEE Transactions on Medical Imaging 41
2022
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Son, J.: Contrastive learning for space-time correspondence via self-cycle consistency. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 14679–14688 (June 2022)
2022
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Zou, Y., Jeong, J., Pemula, L., Zhang, D., Dabeer, O.: Spot-the-difference self-supervised pre-training for anomaly detection and segmentation. In: European Conference on Computer Vision. pp. 392–408. Springer (2022)
2022
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Carstens, M., Rinner, F.M., Bodenstedt, S., Jenke, A.C., Weitz, J., Distler, M., Speidel, S., Kolbinger, F.R.: The dresden surgical anatomy dataset for abdominal organ segmentation in surgical data science. Scientific Data 10
2023
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Catalano, N., Matteucci, M.: Few shot semantic segmentation: a review of methodologies and open challenges. CoRR (2023)
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2023
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2023
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Alapatt, D., Murali, A., Srivastav, V., Consortium, A., Mascagni, P., Padoy, N.: Jumpstarting surgical computer vision. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 328–338. Springer (2024)
2024
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Batić, D., Holm, F., Özsoy, E., Czempiel, T., Navab, N.: Endovit: pretraining vision transformers on a large collection of endoscopic images. International Journal of Computer Assisted Radiology and Surgery 19
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2024
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Leng, T., Zhang, Y., Han, K., Xie, X.: Self-sampling meta sam: enhancing few-shot medical image segmentation with meta-learning. In: Proceedings of the IEEE/CVF winter conference on applications of computer vision. pp. 7925–7935 (2024)
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Liu, Y., Zeng, J., Tao, X., Fang, G.: Rethinking self-supervised semantic segmentation: Achieving end-to-end segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence (2024)
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Matasyoh, N.M., Mathis-Ullrich, F., Zeineldin, R.A.: Samsurg: Surgical instrument segmentation in robotic surgeries using vision foundation model. IEEE Access (2024)
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Sun, Y., Chen, J., Zhang, S., Zhang, X., Chen, Q., Zhang, G., Ding, E., Wang, J., Li, Z.: Vrp-sam: Sam with visual reference prompt. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 23565–23574 (2024)
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2024
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Xie, W., Willems, N., Patil, S., Li, Y., Kumar, M.: Sam fewshot finetuning for anatomical segmentation in medical images. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). pp. 3253–3261 (January 2024)
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Xu, J., LiXiaokang, Chengyuyue, Ma, C., Guo, Y., Wang, Y.: SAM-MPA: Applying SAM to few-shot medical image segmentation using mask propagation and auto-prompting. In: Advancements In Medical Foundation Models: Explainability, Robustness, Security, and Beyond (2024), https://openreview.net/forum?id=IjZI80PUdr
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Zhang, A., Gao, G., Jiao, J., Liu, C., Wei, Y.: Bridge the points: Graph-based few-shot segment anything semantically. Advances in Neural Information Processing Systems 37
2024
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Pavone, M., Baby, B., Carles, E., Innocenzi, C., Baroni, A., Arboit, L., Murali, A., Rosati, A., Iacobelli, V., Fagotti, A., et al.: Introducing the critical view of safety assessment in sentinel node dissection for uterine malignancies: A step toward the use of artificial intelligence to enhance surgical safety and lymph node detection (lyse). International Journal of Gynecological Cancer 35
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