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Encoder-Decoder Network for Brain Tumor Segmentation on Multi-sequence MRI.

, , , and . BrainLes@MICCAI (2), volume 11993 of Lecture Notes in Computer Science, page 296-302. Springer, (2019)

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Squeeze-and-Excitation Normalization for Brain Tumor Segmentation., , , and . BrainLes@MICCAI (2), volume 12659 of Lecture Notes in Computer Science, page 366-373. Springer, (2020)Overview of the HECKTOR Challenge at MICCAI 2021: Automatic Head and Neck Tumor Segmentation and Outcome Prediction in PET/CT Images., , , , , , , , , and 1 other author(s). HECKTOR@MICCAI, volume 13209 of Lecture Notes in Computer Science, page 1-37. Springer, (2021)Head and neck tumor segmentation in PET/CT: The HECKTOR challenge., , , , , , , , , and 17 other author(s). Medical Image Anal., (2022)Non-stationary fuzzy Markov chain., , , and . Pattern Recognit. Lett., 28 (16): 2201-2208 (2007)Encoder-Decoder Network for Brain Tumor Segmentation on Multi-sequence MRI., , , and . BrainLes@MICCAI (2), volume 11993 of Lecture Notes in Computer Science, page 296-302. Springer, (2019)Prognostic value of multimodal MRI tumor features in Glioblastoma multiforme using textural features analysis., , , , and . ISBI, page 50-54. IEEE, (2015)Regarding "Segmentation of heterogeneous or small FDG PET positive tissue based on a 3D-locally adaptive random walk algorithm" By DP. Onoma et al., and . Comput. Medical Imaging Graph., (2015)Prognosis classification in glioblastoma multiforme using multimodal MRI derived heterogeneity textural features: impact of pre-processing choices., , , , and . Medical Imaging: Computer-Aided Diagnosis, volume 9785 of SPIE Proceedings, page 97850W. SPIE, (2016)Image Enhancement With PDEs and Nonconservative Advection Flow Fields., , , , , and . IEEE Trans. Image Process., 28 (6): 3075-3088 (2019)Conditional partial volume correction for emission tomography: A wavelet-based hidden Markov model and multi-resolution approach., , , , , , , and . ISBI, page 1319-1322. IEEE, (2008)