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A Novel Extension to Fuzzy Connectivity for Body Composition Analysis: Applications in Thigh, Brain, and Whole Body Tissue Segmentation.

, , , , , , , , , and . CoRR, (2018)

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Deep Learning for Musculoskeletal Image Analysis., , , and . CoRR, (2020)Neural Transformers for Intraductal Papillary Mucosal Neoplasms (IPMN) Classification in MRI images., , , , , , , , , and 2 other author(s). EMBC, page 475-479. IEEE, (2022)Deep Learning for Musculoskeletal Image Analysis., , , and . ACSSC, page 1481-1485. IEEE, (2019)Capsules for biomedical image segmentation., , , , and . Medical Image Anal., (2021)A Novel Extension to Fuzzy Connectivity for Body Composition Analysis: Applications in Thigh, Brain, and Whole Body Tissue Segmentation., , , , , , , , , and . CoRR, (2018)Otomatik gürbüz bölütleme ile uyluk MR imgelerinde kas ve yağ miktarlarının belirlenmesi (Quantification of muscle and fat volumes in the thigh MR images using automatic robust segmentation). Ege University, Turkey, (2017)Hierarchical 3D Feature Learning forPancreas Segmentation., , , , , and . MLMI@MICCAI, volume 12966 of Lecture Notes in Computer Science, page 238-247. Springer, (2021)Semi-Supervised Deep Learning for Multi-Tissue Segmentation from Multi-Contrast MRI., , , , , , , , and . J. Signal Process. Syst., 94 (5): 497-510 (2022)A Novel Extension to Fuzzy Connectivity for Body Composition Analysis: Applications in Thigh, Brain, and Whole Body Tissue Segmentation., , , , , , , , , and . IEEE Trans. Biomed. Eng., 66 (4): 1069-1081 (2019)Multi-Contrast MRI Segmentation Trained on Synthetic Images., , , and . EMBC, page 5030-5034. IEEE, (2022)