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dAUTOMAP: decomposing AUTOMAP to achieve scalability and enhance performance.

J. Schlemper, I. Öksüz, J. Clough, J. Duan, A. King, J. Schnabel, J. Hajnal, and D. Rueckert. CoRR, (2019)

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Bernd Schlemper

Birgit Schlemper

Klaus Schlemper

Helmut Schlemper

Other publications of authors with the same name

Weakly Supervised Estimation of Shadow Confidence Maps in Fetal Ultrasound Imaging.Q. Meng, J. Housden, J. Matthew, D. Rueckert, J. Schnabel, B. Kainz, M. Sinclair, V. Zimmer, B. Hou, M. Rajchl and 4 other author(s). IEEE Trans. Medical Imaging, 38 (12): 2755-2767 (2019)Dual-domain self-supervised learning for accelerated non-Cartesian MRI reconstruction.B. Zhou, J. Schlemper, N. Dey, S. Salehi, K. Sheth, C. Liu, J. Duncan, and M. Sofka. Medical Image Anal., (2022)Attention gated networks: Learning to leverage salient regions in medical images.J. Schlemper, O. Oktay, M. Schaap, M. Heinrich, B. Kainz, B. Glocker, and D. Rueckert. Medical Image Anal., (2019)k-t NEXT: Dynamic MR Image Reconstruction Exploiting Spatio-Temporal Correlations.C. Qin, J. Schlemper, J. Duan, G. Seegoolam, A. Price, J. Hajnal, and D. Rueckert. MICCAI (2), volume 11765 of Lecture Notes in Computer Science, page 505-513. Springer, (2019)Joint Motion Estimation and Segmentation from Undersampled Cardiac MR Image.C. Qin, W. Bai, J. Schlemper, S. Petersen, S. Piechnik, S. Neubauer, and D. Rueckert. MLMIR@MICCAI, volume 11074 of Lecture Notes in Computer Science, page 55-63. Springer, (2018)Σ-net: Systematic Evaluation of Iterative Deep Neural Networks for Fast Parallel MR Image Reconstruction.K. Hammernik, J. Schlemper, C. Qin, J. Duan, R. Summers, and D. Rueckert. CoRR, (2019)Data consistency networks for (calibration-less) accelerated parallel MR image reconstruction.J. Schlemper, J. Duan, C. Ouyang, C. Qin, J. Caballero, J. Hajnal, and D. Rueckert. CoRR, (2019)Joint Learning of Motion Estimation and Segmentation for Cardiac MR Image Sequences.C. Qin, W. Bai, J. Schlemper, S. Petersen, S. Piechnik, S. Neubauer, and D. Rueckert. MICCAI (2), volume 11071 of Lecture Notes in Computer Science, page 472-480. Springer, (2018)Evaluation of the Robustness of Learned MR Image Reconstruction to Systematic Deviations Between Training and Test Data for the Models from the fastMRI Challenge.P. Johnson, G. Jeong, K. Hammernik, J. Schlemper, C. Qin, J. Duan, D. Rueckert, J. Lee, N. Pezzotti, E. de Weerdt and 31 other author(s). MLMIR@MICCAI, volume 12964 of Lecture Notes in Computer Science, page 25-34. Springer, (2021)DSFormer: A Dual-domain Self-supervised Transformer for Accelerated Multi-contrast MRI Reconstruction.B. Zhou, N. Dey, J. Schlemper, S. Salehi, C. Liu, J. Duncan, and M. Sofka. WACV, page 4955-4964. IEEE, (2023)

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Disambiguation of "Schlemper, Jo"

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dAUTOMAP: decomposing AUTOMAP to achieve scalability and enhance performance.

Please choose a person to relate this publication to

Bernd Schlemper

Birgit Schlemper

Klaus Schlemper

Klaus Schlemper

Helmut Schlemper

Other publications of authors with the same name

Disambiguation

BibSonomy

Disambiguation of "Schlemper, Jo"

copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML dAUTOMAP: decomposing AUTOMAP to achieve scalability and enhance performance.

Please choose a person to relate this publication to

Bernd Schlemper

Birgit Schlemper

Klaus Schlemper

Klaus Schlemper

Helmut Schlemper

Other publications of authors with the same name

Disambiguation

copy delete add this publication to your clipboard
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URL
DOI
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APA
Chicago
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dAUTOMAP: decomposing AUTOMAP to achieve scalability and enhance performance.