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Imaging Graph."},{"key":"10.1016\/j.displa.2026.103532_b0025","series-title":"In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","first-page":"3431","article-title":"Fully convolutional networks for semantic segmentation","author":"Long","year":"2015"},{"key":"10.1016\/j.displa.2026.103532_b0030","series-title":"Medical Image Computing and Computer-Assisted intervention\u2013MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18","first-page":"2015","article-title":"U-net: Convolutional networks for biomedical image segmentation","author":"Ronneberger","year":"2015"},{"key":"10.1016\/j.displa.2026.103532_b0035","series-title":"Computational Methods and Clinical Applications for Spine Imaging: 5th International Workshop and Challenge, CSI Held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, Revised Selected Papers 5","first-page":"2019","article-title":"Automated segmentation of intervertebral disc using fully dilated separable deep neural networks","author":"Wang","year":"2018"},{"key":"10.1016\/j.displa.2026.103532_b0040","series-title":"Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2018: 21st International Conference, Granada, Spain, September 16-20, 2018, Proceedings, Part II 11","first-page":"21","article-title":"SLSDeep: Skin lesion segmentation based on dilated residual and pyramid pooling networks","author":"Sarker","year":"2018"},{"key":"10.1016\/j.displa.2026.103532_b0045","series-title":"Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2018: 21st International Conference, Granada, Spain, September 16-20, 2018, Proceedings, Part IV 11","first-page":"737","article-title":"Star shape prior in fully convolutional networks for skin lesion segmentation","author":"Mirikharaji","year":"2018"},{"key":"10.1016\/j.displa.2026.103532_b0050","unstructured":"F. 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Appl."},{"key":"10.1016\/j.displa.2026.103532_b0180","doi-asserted-by":"crossref","DOI":"10.1016\/j.neucom.2023.126298","article-title":"A review of deep learning segmentation methods for carotid artery ultrasound images","volume":"545","author":"Huang","year":"2023","journal-title":"Neurocomputing"},{"key":"10.1016\/j.displa.2026.103532_b0185","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2023.106718","article-title":"NAG-Net: Nested attention-guided learning for segmentation of carotid lumen-intima interface and media-adventitia interface","volume":"156","author":"Huang","year":"2023","journal-title":"Comput. Biol. 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Intell."},{"key":"10.1016\/j.displa.2026.103532_b0280","doi-asserted-by":"crossref","unstructured":"N. Ma, X. Zhang, H. Zheng, and J. Sun, \u201cShuffleNet V2: Practical guidelines for efficient CNN architecture design,\u201d ECCV, vol. abs\/1807.11164, pp. 116-131. Jul. 2018, doi: 10.1007\/978-3-030-01264-9_8.","DOI":"10.1007\/978-3-030-01264-9_8"},{"key":"10.1016\/j.displa.2026.103532_b0285","series-title":"Proceedings of Twentieth Euromicro Conference. 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Tschandl, M.E. Celebi, S. Dusza, D. Gutman, B. Helba, A. Kalloo, K. Liopyris, M. Marchetti, H. Kittler, Skin lesion analysis toward melanoma detection 2018: A challenge hosted by the international skin imaging collaboration (isic). arXiv preprint arXiv:1902.03368. 2019."},{"key":"10.1016\/j.displa.2026.103532_b0310","series-title":"35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)","first-page":"5437","article-title":"PH 2-a dermoscopic image database for research and benchmarking","author":"Mendon\u00e7a","year":"2013"},{"key":"10.1016\/j.displa.2026.103532_b0315","article-title":"Pytorch: an imperative style, high-performance deep learning library","volume":"32","author":"Paszke","year":"2019","journal-title":"Adv. Neural Inf. Proces. Syst."},{"key":"10.1016\/j.displa.2026.103532_b0320","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Liu, H. and Hu, Q., 2021. 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