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Attention u-net: Learning where to look for the pancreas

O. Öktay, J. "Schlemper, L. "Folgoc, M. "Lee, M. "Heinrich, K. "Misawa, K. "Mori, S. "McDonagh, N. "Hammerla, and B. "Kainz. (2018)

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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)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)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)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)Bayesian Deep Learning for Accelerated MR Image Reconstruction.J. Schlemper, D. de Castro, W. Bai, C. Qin, O. Oktay, J. Duan, A. Price, J. Hajnal, and D. Rueckert. MLMIR@MICCAI, volume 11074 of Lecture Notes in Computer Science, page 64-71. Springer, (2018)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)

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Attention u-net: Learning where to look for the pancreas

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copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Attention u-net: Learning where to look for the pancreas

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Attention u-net: Learning where to look for the pancreas