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Recent advances in AI foundation models have significant potential for lightening the clinical workload by mimicking the comprehensive and multi-faceted approaches used by medical professionals.
Lin, C.Y.: Rouge: A package for automatic evaluation of summaries. In: Text summarization branches out. pp. 74–81 (2004)
2004
Earlier work this paper cites.
Crum, W.R., Camara, O., Hill, D.L.: Generalized overlap measures for evaluation and validation in medical image analysis. IEEE transactions on medical imaging 25
2006
Earlier work this paper cites.
2016
Earlier work this paper cites.
Çiçek, Ö., Abdulkadir, A., Lienkamp, S.S., Brox, T., Ronneberger, O.: 3D U-Net: learning dense volumetric segmentation from sparse annotation. In: Medical Image Computing and Computer-Assisted Intervention. pp. 424–432. Springer (2016)
2016
Earlier work this paper cites.
2017
Earlier work this paper cites.
Hosny, A., Parmar, C., Quackenbush, J., Schwartz, L.H., Aerts, H.J.: Artificial intelligence in radiology. Nature Reviews Cancer 18
2018
Earlier work this paper cites.
2019
Earlier work this paper cites.
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33
2020
Earlier work this paper cites.
Choi, M.S., Choi, B.S., Chung, S.Y., Kim, N., Chun, J., Kim, Y.B., Chang, J.S., Kim, J.S.: Clinical evaluation of atlas-and deep learning-based automatic segmentation of multiple organs and clinical target volumes for breast cancer. Radiotherapy and Oncology 153
2020
Earlier work this paper cites.
Jiang, H., He, P., Chen, W., Liu, X., Gao, J., Zhao, T.: Smart: Robust and efficient fine-tuning for pre-trained natural language models through principled regularized optimization. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. pp. 2177–2190 (2020)
2020
Earlier work this paper cites.
Zhu, C., Cheng, Y., Gan, Z., Sun, S., Goldstein, T., Liu, J.: Freelb: Enhanced adversarial training for natural language understanding. In: International Conference on Learning Representations (2020)
2020
Earlier work this paper cites.
Aghajanyan, A., Shrivastava, A., Gupta, A., Goyal, N., Zettlemoyer, L., Gupta, S.: Better fine-tuning by reducing representational collapse. In: International Conference on Learning Representations (2021)
2021
Earlier work this paper cites.
Chung, S.Y., Chang, J.S., Choi, M.S., Chang, Y., Choi, B.S., Chun, J., Keum, K.C., Kim, J.S., Kim, Y.B.: Clinical feasibility of deep learning-based auto-segmentation of target volumes and organs-at-risk in breast cancer patients after breast-conserving surgery. Radiation Oncology 16
2021
Earlier work this paper cites.
Hua, H., Li, X., Dou, D., Xu, C., Luo, J.: Noise stability regularization for improving bert fine-tuning. In: Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. pp. 3229–3241 (2021)
2021
Earlier work this paper cites.
OpenAI: Chatgpt. OpenAI Blog (2021)
2021
Earlier work this paper cites.
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
Earlier work this paper cites.
Li, B., Weinberger, K.Q., Belongie, S., Koltun, V., Ranftl, R.: Language-driven semantic segmentation. In: International Conference on Learning Representations (2022)
2022
Earlier work this paper cites.
Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in neural information processing systems 35
2022
Earlier work this paper cites.
2022
Cited alongside, same era.
Wang, Z., Lu, Y., Li, Q., Tao, X., Guo, Y., Gong, M., Liu, T.: Cris: Clip-driven referring image segmentation. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 11686–11695 (2022)
2022
Cited alongside, same era.
Zhou, K., Yang, J., Loy, C.C., Liu, Z.: Conditional prompt learning for vision-language models. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 16816–16825 (2022)
2022
Cited alongside, same era.
2023
Cited alongside, same era.
Moor, M., Banerjee, O., Abad, Z.S.H., Krumholz, H.M., Leskovec, J., Topol, E.J., Rajpurkar, P.: Foundation models for generalist medical artificial intelligence. Nature 616
2023
Closest in time.
OpenAI: GPT-4 Technical Report arxiv/2303.08774
2023
Closest in time.
Rajpurkar, P., Lungren, M.P.: The Current and Future State of AI Interpretation of Medical Images. New England Journal of Medicine 388
2023
Closest in time.
Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620
2023
Closest in time.
Taori, R., Gulrajani, I., Zhang, T., Dubois, Y., Li, X., Guestrin, C., Liang, P., Hashimoto, T.B.: Stanford alpaca: An instruction-following llama model (2023)
2023
Closest in time.
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Chiang, W.L., Li, Z., Lin, Z., Sheng, Y., Wu, Z., Zhang, H., Zheng, L., Zhuang, S., Zhuang, Y., Gonzalez, J.E., et al.: Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality. See https://vicuna. lmsys. org (accessed 14 April 2023) (2023)
2023
Cited alongside, same era.
Conover, M., Hayes, M., Mathur, A., Xie, J., Wan, J., Shah, S., Ghodsi, A., Wendell, P., Zaharia, M., Xin, R.: Free dolly: Introducing the world’s first truly open instruction-tuned llm (2023)
2023
Cited alongside, same era.
Ding, Z., Wang, J., Tu, Z.: Open-vocabulary universal image segmentation with maskclip. In: Proceedings of the 40th International Conference on Machine Learning. pp. 8090–8102 (2023)
2023
Cited alongside, same era.
Driess, D., Xia, F., Sajjadi, M.S.M., Lynch, C., Chowdhery, A., Ichter, B., Wahid, A., Tompson, J., Vuong, Q., Yu, T., Huang, W., Chebotar, Y., Sermanet, P., Duckworth, D., Levine, S., Vanhoucke, V., Hausman, K., Toussaint, M., Greff, K., Zeng, A., Mordatch, I., Florence, P.: Palm-e: An embodied multimodal language model (2023)
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Cited alongside, same era.
Kirillov, A., Mintun, E., Ravi, N., Mao, H., Rolland, C., Gustafson, L., Xiao, T., Whitehead, S., Berg, A.C., Lo, W.Y., et al.: Segment anything. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 4015–4026 (2023)
2023
Cited alongside, same era.
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
Dai, W., Li, J., Li, D., Tiong, A.M.H., Zhao, J., Wang, W., Li, B., Fung, P.N., Hoi, S.: Instructblip: Towards general-purpose vision-language models with instruction tuning. Advances in Neural Information Processing Systems 36
2024
Closest in time.
Jain, N., yeh Chiang, P., Wen, Y., Kirchenbauer, J., Chu, H.M., Somepalli, G., Bartoldson, B.R., Kailkhura, B., Schwarzschild, A., Saha, A., Goldblum, M., Geiping, J., Goldstein, T.: NEFTune: Noisy embeddings improve instruction finetuning. In: The Twelfth International Conference on Learning Representations (2024)
2024
Closest in time.
Joshi, G., Jain, A., Araveeti, S.R., Adhikari, S., Garg, H., Bhandari, M.: FDA-Approved Artificial Intelligence and Machine Learning (AI/ML)-Enabled Medical Devices: An Updated Landscape. Electronics 13
2024
Closest in time.
2024
Closest in time.
Lai, X., Tian, Z., Chen, Y., Li, Y., Yuan, Y., Liu, S., Jia, J.: Lisa: Reasoning segmentation via large language model. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 9579–9589 (2024)
2024
Closest in time.
Liu, H., Li, C., Wu, Q., Lee, Y.J.: Visual instruction tuning. Advances in neural information processing systems 36
2024
Closest in time.
Oh, Y., Park, S., Byun, H.K., Cho, Y., Lee, I.J., Kim, J.S., Ye, J.C.: Llm-driven multimodal target volume contouring in radiation oncology. Nature Communications 15
2024
Closest in time.
Smine, Z., Poeta, S., De Caluwé, A., Desmet, A., Garibaldi, C., Boni, K.B., Levillain, H., Van Gestel, D., Reynaert, N., Dhont, J.: Automated segmentation in planning-ct for breast cancer radiotherapy: A review of recent advances. Radiotherapy and Oncology p. 110615 (2024)
2024
Closest in time.
Tu, T., Azizi, S., Driess, D., Schaekermann, M., Amin, M., Chang, P.C., Carroll, A., Lau, C., Tanno, R., Ktena, I., et al.: Towards generalist biomedical ai. NEJM AI 1
2024
Closest in time.