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Learning deep spatio-temporal dependence for semantic video segmentation. IEEE Trans Multimedia. 2018;20(4):939-949. [Crossref]","DOI":"10.1109\/TMM.2017.2759504"},{"key":"ref5","unstructured":"6. Zhang Z, Liu Q, Wang Y. Road extraction by deep residual U-Net. Arxiv. [Crossref]"},{"key":"ref6","unstructured":"7. Jha D, Smedsrud PH, Riegler MA, et al. ResUNet++: an advanced architecture for medical image segmentation. Arxiv. [Crossref]"},{"key":"ref7","unstructured":"8. Chollet F. 2015. Keras. GitHub. [Crossref]"},{"key":"ref8","unstructured":"9. Keeton K. Proceedings of the 12th USENIX conference on operating systems design and implementation. USENIX Association; 2016. [Crossref]"},{"key":"ref9","doi-asserted-by":"publisher","unstructured":"10. Yousef R, Khan S, Gupta G, et al. U-Net-based models towards optimal MR brain image segmentation. Diagnostics. 2023;13(9):1624. [Crossref]","DOI":"10.3390\/diagnostics13091624"},{"key":"ref10","doi-asserted-by":"publisher","unstructured":"11. 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