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Self-supervised Recurrent Neural Network for 4D Abdominal and In-utero MR Imaging.

T. Zhang, L. Jackson, A. Uus, J. Clough, L. Story, M. Rutherford, J. Hajnal, and M. Deprez. MLMIR@MICCAI, volume 11905 of Lecture Notes in Computer Science, page 16-24. Springer, (2019)

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Ivo Hajnal

Hajnal Türk

David Hajnal

Hajnal Borús

V Saile

Other publications of authors with the same name

Non-linear Registration with the Variable Viscosity Fluid Algorithm.H. Lester, S. Arridge, K. Jansons, L. Lemieux, J. Hajnal, and A. Oatridge. IPMI, volume 1613 of Lecture Notes in Computer Science, page 238-251. Springer, (1999)Using a Maximum Uncertainty LDA-Based Approach to Classify and Analyse MR Brain Images.C. Thomaz, J. Boardman, D. Hill, J. Hajnal, D. Edwards, M. Rutherford, D. Gillies, and D. Rueckert. MICCAI (1), volume 3216 of Lecture Notes in Computer Science, page 291-300. Springer, (2004)Interpolation Artefacts in Non-rigid Registration.P. Aljabar, J. Hajnal, R. Boyes, and D. Rueckert. MICCAI (2), volume 3750 of Lecture Notes in Computer Science, page 247-254. Springer, (2005)Reconstruction of fetal brain MRI with intensity matching and complete outlier removal.M. Kuklisova-Murgasova, G. Quaghebeur, M. Rutherford, J. Hajnal, and J. Schnabel. Medical Image Anal., 16 (8): 1550-1564 (2012)The effect of preterm birth on neonatal cerebral vasculature studied with magnetic resonance angiography at 3 Tesla.C. Malamateniou, S. Counsell, J. Allsop, J. Fitzpatrick, L. Srinivasan, F. Cowan, J. Hajnal, and M. Rutherford. NeuroImage, 32 (3): 1050-1059 (2006)An Evaluation of Deformation-Based Morphometry Applied to the Developing Human Brain and Detection of Volumetric Changes Associated with Preterm Birth.J. Boardman, K. Bhatia, S. Counsell, J. Allsop, O. Kapellou, M. Rutherford, A. Edwards, J. Hajnal, and D. Rueckert. MICCAI (1), volume 2878 of Lecture Notes in Computer Science, page 697-704. Springer, (2003)Normalisation of Neonatal Brain Network Measures Using Stochastic Approaches.M. Schirmer, G. Ball, S. Counsell, A. Edwards, D. Rueckert, J. Hajnal, and P. Aljabar. MICCAI (1), volume 8149 of Lecture Notes in Computer Science, page 574-581. Springer, (2013)Classifier Selection Strategies for Label Fusion Using Large Atlas Databases.P. Aljabar, R. Heckemann, A. Hammers, J. Hajnal, and D. Rueckert. MICCAI (1), volume 4791 of Lecture Notes in Computer Science, page 523-531. Springer, (2007)dStripe: Slice artefact correction in diffusion MRI via constrained neural network.M. Pietsch, D. Christiaens, J. Hajnal, and J. Tournier. Medical Image Anal., (2021)A deformable model for the reconstruction of the neonatal cortex.A. Schuh, A. Makropoulos, R. Wright, E. Robinson, N. Tusor, J. Steinweg, E. Hughes, L. Cordero-Grande, A. Price, J. Hutter and 2 other author(s). ISBI, page 800-803. IEEE, (2017)

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Disambiguation of "Hajnal, Joseph V."

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Self-supervised Recurrent Neural Network for 4D Abdominal and In-utero MR Imaging.

Please choose a person to relate this publication to

Ivo Hajnal

Hajnal Türk

David Hajnal

Hajnal Borús

V Saile

Other publications of authors with the same name

Disambiguation

BibSonomy

Disambiguation of "Hajnal, Joseph V."

copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Self-supervised Recurrent Neural Network for 4D Abdominal and In-utero MR Imaging.

Please choose a person to relate this publication to

Ivo Hajnal

Hajnal Türk

David Hajnal

Hajnal Borús

V Saile

Other publications of authors with the same name

Disambiguation

copy delete add this publication to your clipboard
community post
history of this post
URL
DOI
BibTeX
EndNote
APA
Chicago
DIN 1505
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MSOffice XML

Self-supervised Recurrent Neural Network for 4D Abdominal and In-utero MR Imaging.