MedGround-R1: Advancing Medical Image Grounding via Spatial-Semantic Rewarded Group Relative Policy Optimization

Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017)

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2017

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Johnson, A.E., Pollard, T.J., Berkowitz, S.J., Greenbaum, N.R., Lungren, M.P., Deng, C.y., Mark, R.G., Horng, S.: Mimic-cxr, a de-identified publicly available database of chest radiographs with free-text reports. Scientific data 6

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Yu, L., Zhang, Z., Li, X., Ren, H., Zhao, W., Xing, L.: Metal artifact reduction in 2d ct images with self-supervised cross-domain learning. Physics in Medicine & Biology 66

2021

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Boecking, B., Usuyama, N., Bannur, S., Castro, D.C., Schwaighofer, A., Hyland, S., Wetscherek, M., Naumann, T., Nori, A., Alvarez-Valle, J., et al.: Making the most of text semantics to improve biomedical vision–language processing. In: European conference on computer vision. pp. 1–21. Springer (2022)

2022

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Wang, Z., Wu, Z., Agarwal, D., Sun, J.: Medclip: Contrastive learning from unpaired medical images and text. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing. Conference on Empirical Methods in Natural Language Processing. vol. 2022, p. 3876 (2022)

2022

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Chen, Z., Zhou, Y., Tran, A., Zhao, J., Wan, L., Ooi, G.S.K., Cheng, L.T.E., Thng, C.H., Xu, X., Liu, Y., et al.: Medical phrase grounding with region-phrase context contrastive alignment. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 371–381. Springer (2023)

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Li, Z., Li, Y., Li, Q., Wang, P., Guo, D., Lu, L., Jin, D., Zhang, Y., Hong, Q.: Lvit: language meets vision transformer in medical image segmentation. IEEE transactions on medical imaging 43

2023

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Tian, X., Ng, W.W., Xu, H.: Deep incremental hashing for semantic image retrieval with concept drift. IEEE Transactions on Big Data 9

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2023

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Zhong, Y., Xu, M., Liang, K., Chen, K., Wu, M.: Ariadne’s thread: Using text prompts to improve segmentation of infected areas from chest x-ray images. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 724–733. Springer (2023)

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Bai, F., Du, Y., Huang, T., Meng, M.Q.H., Zhao, B.: M3d: Advancing 3d medical image analysis with multi-modal large language models. arXiv preprint arXiv:2404.00578 (2024)

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2024

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Chen, Y., Wei, M., Zheng, Z., Hu, J., Shi, Y., Xiong, S., Zhu, X.X., Mou, L.: Causalclipseg: Unlocking clip’s potential in referring medical image segmentation with causal intervention. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 77–87. Springer (2024)

2024

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Hu, M., Yuan, K., Shen, Y., Tang, F., Xu, X., Zhou, L., Li, W., Chen, Y., Xu, Z., Peng, Z., et al.: Ophclip: Hierarchical retrieval-augmented learning for ophthalmic surgical video-language pretraining. arXiv preprint arXiv:2411.15421 (2024)

Wang, L., Wang, H., Yang, H., Mao, J., Yang, Z., Shen, J., Li, X.: Interpretable bilingual multimodal large language model for diverse biomedical tasks. arXiv preprint arXiv:2410.18387 (2024)

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2024

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Wu, H., Yang, Y., Xu, H., Wang, W., Zhou, J., Zhu, L.: Rainmamba: Enhanced locality learning with state space models for video deraining. In: Proceedings of the 32nd ACM International Conference on Multimedia. pp. 7881–7890 (2024)

2024

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Xu, H., Yang, Y., Aviles-Rivero, A.I., Yang, G., Qin, J., Zhu, L.: Lgrnet: Local-global reciprocal network for uterine fibroid segmentation in ultrasound videos. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 667–677. Springer (2024)

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Xu, H., Zhu, L.: Online agglomerative pooling for scalable self-supervised universal segmentation (2024), https://openreview.net/forum?id=d32d9fE5lG

2024