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SA-LuT-Nets: Learning Sample-Adaptive Intensity Lookup Tables for Brain Tumor Segmentation., , , , , , and . IEEE Trans. Medical Imaging, 40 (5): 1417-1427 (2021)Going deeper with brain morphometry using neural networks., , , , , , , and . CoRR, (2020)Evaluation of novel data-driven metrics of amyloid β deposition for longitudinal PET studies., , , , , , , , , and 3 other author(s). NeuroImage, (October 2023)Efficient machine learning framework for computer-aided detection of cerebral microbleeds using the Radon transform., , , , , , and . ISBI, page 113-116. IEEE, (2014)A normalisation framework for quantitative brain imaging; application to quantitative susceptibility mapping., , , , , , , , , and 2 other author(s). ISBI, page 97-100. IEEE, (2017)PET image harmonization using smoothing-CycleGAN., , , , , , and . Medical Imaging: Computer-Aided Diagnosis, volume 12033 of SPIE Proceedings, SPIE, (2022)An Informatics Platform for the Management of Data for Australian Dementia Network (ADNeT) Initiative., , , , and . MedInfo, volume 310 of Studies in Health Technology and Informatics, page 1364-1365. IOS Press, (2023)3D Brain MRI GAN-Based Synthesis Conditioned on Partial Volume Maps., , , , , , and . SASHIMI@MICCAI, volume 12417 of Lecture Notes in Computer Science, page 11-20. Springer, (2020)Expectation-Maximization with Image-Weighted Markov Random Fields to Handle Severe Pathology., , , , , and . DICTA, page 1-6. IEEE, (2015)Nonrigid correction of interleaving artefacts in pelvic MRI., , , , , , , , , and 2 other author(s). Medical Imaging: Image Processing, volume 7259 of SPIE Proceedings, page 72592P. SPIE, (2009)