Challenge Summary U-MedSAM: Uncertainty-aware MedSAM for Medical Image Segmentation

Kendall, A., Gal, Y., Cipolla, R.: Multi-task learning using uncertainty to weigh losses for scene geometry and semantics. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 7482–7491 (2018)

2018

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Zhao, X., Hu, S., Cho, J.H., Chen, F.: Uncertainty-based decision making using deep reinforcement learning. In: 2019 22th International Conference on Information Fusion (FUSION). pp. 1–8. IEEE (2019)

2019

Earlier work this paper cites.

Jadon, S.: A survey of loss functions for semantic segmentation. In: 2020 IEEE conference on computational intelligence in bioinformatics and computational biology (CIBCB). pp. 1–7. IEEE (2020)

2020

Earlier work this paper cites.

Zhao, X., Chen, F., Hu, S., Cho, J.H.: Uncertainty aware semi-supervised learning on graph data. Advances in Neural Information Processing Systems 33

2020

Earlier work this paper cites.

Ma, J., Chen, J., Ng, M., Huang, R., Li, Y., Li, C., Yang, X., Martel, A.L.: Loss odyssey in medical image segmentation. Medical Image Analysis 71

2021

Earlier work this paper cites.

Cho, M.S., Nguyen, T.: Improving segmentation with advanced loss functions. Journal of Computational Diagnostics (2022)

2022

Earlier work this paper cites.

Harper, J., Stone, M.: SAM optimizer: Advancing model generalization. Journal of Machine Learning Research (2022)

2022

Earlier work this paper cites.

Murray, A., et al.: Deep learning in medical imaging: Overfitting issues. Medical Image Analysis (2022)

2022

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Xu, Z., Escalera, S., Pavão, A., Richard, M., Tu, W.W., Yao, Q., Zhao, H., Guyon, I.: Codabench: Flexible, easy-to-use, and reproducible meta-benchmark platform. Patterns 3

2022

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Chalcroft, L., Pereira, R.L., Brudfors, M., Kayser, A.S., D’Esposito, M., Price, C.J., Pappas, I., Ashburner, J.: Large-kernel attention for efficient and robust brain lesion segmentation. arXiv preprint arXiv:2308.07251 (2023)

Original

2023

Earlier work this paper cites.

Hu, S., Wang, X., Lyu, S.: Rank-based decomposable losses in machine learning: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence (2023)

2023

Cited alongside, same era.

Kim, J., Park, S.H.: Multiloss strategy for medical image segmentation. Computational Medicine (2023)

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., Dollar, P., Girshick, R.: Segment anything. In: Proceedings of the International Conference on Computer Vision. pp. 4015–4026 (2023)

2023

Cited alongside, same era.

Wang, X., Luo, Z., Hu, J., Feng, C., Hu, S., Zhu, B., Wu, X., Lyu, S.: Deep reinforcement learning for image-to-image translation. arXiv preprint arXiv:2309.13672 (2023)

Original

2023

Cited alongside, same era.

Zhang, C., Han, D., Qiao, Y., Kim, J.U., Bae, S.H., Lee, S., Hong, C.S.: Faster segment anything: Towards lightweight sam for mobile applications. arXiv preprint arXiv:2306.14289 (2023)

Lin, L., Papabathini, S., Wang, X., Hu, S.: Robust light-weight facial affective behavior recognition with CLIP. MIPR (2024)

2024

Closest in time.

Lin, L., Wang, X., Hu, S., et al.: Ai-face: A million-scale demographically annotated ai-generated face dataset and fairness benchmark. arXiv preprint arXiv:2406.00783 (2024)

Original

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

Closest in time.

Peng, Y., Chen, H., Lin, C., Huang, G., Hu, J., Guo, H., Kong, B., Hu, S., Wu, X., Wang, X.: Uncertainty-Aware explainable recommendation with large language models. IJCNN (2024)

2024

Closest in time.

Tsai, T.Y., Lin, L., Hu, S., Tsao, C.W., Li, X., Chang, M.C., Zhu, H., Wang, X.: Uu-mamba: Uncertainty-aware u-mamba for cardiovascular segmentation. arXiv preprint arXiv:2409.14305 (2024)