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DBCE : A Saliency Method for Medical Deep Learning Through Anatomically-Consistent Free-Form Deformations.

J. Peters, L. Lebrat, R. Cruz, A. Nicolson, G. Belous, S. Konate, P. Raniga, V. Doré, P. Bourgeat, J. Mejan-Fripp, C. Fookes, and O. Salvado. WACV, page 1958-1968. IEEE, (2023)

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Towards a generalization of the MP2RAGE partial volume estimation model to account for B1+ inhomogeneities at 7T.J. Beaumont, O. Acosta, P. Raniga, G. Gambarota, and J. Fripp. ISBI, page 1206-1209. IEEE, (2021)An Approach for Generating Realistic Australian Synthetic Healthcare Data.I. Diouf, J. Grimes, M. O'Brien, H. Hassanzadeh, D. Truran, H. Ngo, P. Raniga, M. Lawley, D. Bauer, D. Hansen and 2 other author(s). MedInfo, volume 310 of Studies in Health Technology and Informatics, page 820-824. IOS Press, (2023)Design, implementation and operation of a multimodality research imaging informatics repository.T. Nguyên, P. Raniga, D. Barnes, and G. Egan. Health Inf. Sci. Syst., (2015)An Informatics Platform for the Management of Data for Australian Dementia Network (ADNeT) Initiative.J. Trinder, P. Bourgeat, Y. Xia, J. Fripp, and P. Raniga. MedInfo, volume 310 of Studies in Health Technology and Informatics, page 1364-1365. IOS Press, (2023)A normalisation framework for quantitative brain imaging; application to quantitative susceptibility mapping.A. Fazlollahi, S. Ayton, P. Bourgeat, P. Raniga, A. Ng, J. Fripp, D. Ames, C. Masters, C. Rowe, V. Villemagne and 2 other author(s). ISBI, page 97-100. IEEE, (2017)Vein segmentation using shape-based Markov Random Fields.P. Ward, N. Ferris, P. Raniga, A. Ng, D. Barnes, D. Dowe, and G. Egan. ISBI, page 1133-1136. IEEE, (2017)Spline Based Inhomogeneity Correction for 11C-PIB PET Segmentation Using Expectation Maximization.P. Raniga, P. Bourgeat, V. Villemagne, G. O'Keefe, C. Rowe, and S. Ourselin. MICCAI (1), volume 4791 of Lecture Notes in Computer Science, page 228-235. Springer, (2007)Local intensity model: An outlier detection framework with applications to white matter hyperintensity segmentation.P. Raniga, P. Schmitt, P. Bourgeat, J. Fripp, V. Villemagne, C. Rowe, and O. Salvado. ISBI, page 2057-2060. IEEE, (2011)The multi-modal Australian ScienceS Imaging and Visualization Environment (MASSIVE) high performance computing infrastructure: applications in neuroscience and neuroinformatics research.W. Goscinski, P. McIntosh, U. Felzmann, A. Maksimenko, C. Hall, T. Gureyev, D. Thompson, A. Janke, G. Galloway, N. Killeen and 8 other author(s). Frontiers Neuroinformatics, (2014)Data Augmentation Using Synthetic Lesions Improves Machine Learning Detection of Microbleeds from MRI.S. Momeni, A. Fazlollahi, P. Bourgeat, P. Raniga, P. Yates, N. Yassi, P. Desmond, J. Fripp, Y. Gao, and O. Salvado. SASHIMI@MICCAI, volume 11037 of Lecture Notes in Computer Science, page 12-19. Springer, (2018)

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DBCE : A Saliency Method for Medical Deep Learning Through Anatomically-Consistent Free-Form Deformations.

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copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML DBCE : A Saliency Method for Medical Deep Learning Through Anatomically-Consistent Free-Form Deformations.

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DBCE : A Saliency Method for Medical Deep Learning Through Anatomically-Consistent Free-Form Deformations.