G-SAM: GMM-based segment anything model for medical image classification and segmentation
Author:
Funder
National Natural Science Foundation of China
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s10586-024-04679-x.pdf
Reference47 articles.
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2. Ouyang, D., He, B., Ghorbani, A., Yuan, N., Ebinger, J., Langlotz, C.P., Heidenreich, P.A., Harrington, R.A., Liang, D.H., Ashley, E.A., et al.: Video-based AI for beat-to-beat assessment of cardiac function. Nature 580(7802), 252–256 (2020). https://doi.org/10.1038/s41586-020-2145-8
3. Graham, S., Vu, Q.D., Jahanifar, M., Raza, S.E.A., Minhas, F., Snead, D., Rajpoot, N.: One model is all you need: multi-task learning enables simultaneous histology image segmentation and classification. Med. Image Anal. 83, 1026 (2023). https://doi.org/10.1016/j.media.2022.102685
4. Girum, K.B., Créhange, G., Lalande, A.: Learning with context feedback loop for robust medical image segmentation. IEEE Trans. Med. Imaging 40(6), 1542–1554 (2021). https://doi.org/10.1109/TMI.2021.3060497
5. Manocha, A., Sood, S.K., Bhatia, M.: Edge intelligence-assisted smart healthcare solution for health pandemic: a federated environment approach. Clust. Comput. (2024). https://doi.org/10.1007/s10586-023-04245-x
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