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Active Volume Models for Medical Image Segmentation.

, , and . IEEE Trans. Medical Imaging, 30 (3): 774-791 (2011)

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Active Volume Models for Medical Image Segmentation., , and . IEEE Trans. Medical Imaging, 30 (3): 774-791 (2011)Deep image synthesis from intuitive user input: A review and perspectives., , , , , and . Comput. Vis. Media, 8 (1): 3-31 (2022)Markup SVG - An Online Content-Aware Image Abstraction and Annotation Tool., , and . IEEE Trans. Multim., 13 (5): 993-1006 (2011)Natural language processing with machine learning methods to analyze unstructured patient-reported outcomes derived from electronic health records: A systematic review., , , , , , , and . Artif. Intell. Medicine, (December 2023)SegAN: Adversarial Network with Multi-scale L1 Loss for Medical Image Segmentation., , , , and . CoRR, (2017)Interactive Polygons in Region-Based Deformable Contours for Medical Images., , , and . ISBI, page 37-40. IEEE, (2009)Adversarial learning with multi-scale loss for skin lesion segmentation., , and . ISBI, page 859-863. IEEE, (2018)Adjustable adaboost classifier and pyramid features for image-based cervical cancer diagnosis., , and . ISBI, page 281-285. IEEE, (2015)Neural User Factor Adaptation for Text Classification: Learning to Generalize Across Author Demographics., and . *SEM@NAACL-HLT, page 136-146. Association for Computational Linguistics, (2019)Smart cook: making cooking easier with multimodal learning., , , , , and . UbiComp/ISWC Adjunct, page 129-132. ACM, (2019)