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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)A Novel Extension to Fuzzy Connectivity for Body Composition Analysis: Applications in Thigh, Brain, and Whole Body Tissue Segmentation., , , , , , , , , and . CoRR, (2018)Capsules for biomedical image segmentation., , , , and . Medical Image Anal., (2021)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)Multi-Contrast MRI Segmentation Trained on Synthetic Images., , , and . EMBC, page 5030-5034. IEEE, (2022)Information Bottleneck Attribution for Visual Explanations of Diagnosis and Prognosis., , , , , , , , , and . MLMI@MICCAI, volume 12966 of Lecture Notes in Computer Science, page 396-405. Springer, (2021)Semi-Supervised Deep Learning for Multi-Tissue Segmentation from Multi-Contrast MRI., , , , , , , , and . J. Signal Process. Syst., 94 (5): 497-510 (2022)