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Recent advances in large foundation models, such as the Segment Anything Model (SAM), have demonstrated considerable promise across various tasks.
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Porwal, P., Pachade, S., Kamble, R., Kokare, M., Deshmukh, G., Sahasrabuddhe, V., Meriaudeau, F.: Indian diabetic retinopathy image dataset (idrid): a database for diabetic retinopathy screening research. Data 3
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Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. In: International Conference on Learning Representations (2020)
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Hu, E.J., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W., et al.: Lora: Low-rank adaptation of large language models. In: International Conference on Learning Representations (2022)
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Rombach, R., Blattmann, A., Lorenz, D., Esser, P., Ommer, B.: High-resolution image synthesis with latent diffusion models. In: Computer Vision and Pattern Recognition. pp. 10684–10695 (2022)
2022
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2023
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Kirillov, A., Mintun, E., Ravi, N., Mao, H., Rolland, C., Gustafson, L., Xiao, T., Whitehead, S., Berg, A.C., Lo, W.Y., Dollar, P., Girshick, R.: Segment anything. In: International Conference on Computer Vision. pp. 4015–4026 (2023)
2023
Cited alongside, same era.
2023
Cited alongside, same era.
Hu, M., Xia, P., Wang, L., Yan, S., Tang, F., Xu, Z., Luo, Y., Song, K., Leitner, J., Cheng, X., Cheng, J., Liu, C., Zhou, K., Ge, Z.: Ophnet: A large-scale video benchmark for ophthalmic surgical workflow understanding (2024)
2024
Closest in time.
Huang, D., Xiong, X., Ma, J., Li, J., Jie, Z., Ma, L., Li, G.: Alignsam: Aligning segment anything model to open context via reinforcement learning. In: Computer Vision and Pattern Recognition. pp. 3205–3215 (2024)
2024
Closest in time.
Ma, J., He, Y., Li, F., Han, L., You, C., Wang, B.: Segment anything in medical images. Nature Communications 15
2024
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Tang, F., Xu, Z., Qu, Z., Feng, W., Jiang, X., Ge, Z.: Hunting attributes: Context prototype-aware learning for weakly supervised semantic segmentation. In: Computer Vision and Pattern Recognition. pp. 3324–3334 (2024)
2024
Closest in time.
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Tragakis, A., Kaul, C., Murray-Smith, R., Husmeier, D.: The fully convolutional transformer for medical image segmentation. In: Winter Conference on Applications of Computer Vision. pp. 3660–3669 (2023)
2023
Cited alongside, same era.
Wang, X., Fang, Y., Yang, S., Zhu, D., Wang, M., Zhang, J., Zhang, J., Cheng, J., Tong, K.y., Han, X.: Clc-net: Contextual and local collaborative network for lesion segmentation in diabetic retinopathy images. Neurocomputing 527
2023
Cited alongside, same era.
2023
Cited alongside, same era.
Xiong, X., Wang, C., Li, W., Li, G.: Mammo-sam: Adapting foundation segment anything model for automatic breast mass segmentation in whole mammograms. In: International Workshop on Machine Learning in Medical Imaging. pp. 176–185. Springer (2023)
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Cited alongside, same era.
Guo, T., Yang, J., Yu, Q.: Diabetic retinopathy lesion segmentation using deep multi-scale framework. Biomedical Signal Processing and Control 88
2024
Cited alongside, same era.
Wang, D., Zhang, J., Du, B., Xu, M., Liu, L., Tao, D., Zhang, L.: Samrs: Scaling-up remote sensing segmentation dataset with segment anything model. Advances in Neural Information Processing Systems 36
2024
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2024
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2024
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2024
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Xing, Z., Zhu, L., Yu, L., Xing, Z., Wan, L.: Hybrid masked image modeling for 3d medical image segmentation. IEEE Journal of Biomedical and Health Informatics (2024)
2024
Closest in time.
Zhang, R., Jiang, Z., Guo, Z., Yan, S., Pan, J., Dong, H., Gao, P., Li, H.: Personalize segment anything model with one shot. In: International Conference on Learning Representations (2024)
2024
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