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A Probabilistic Hadamard U-Net for MRI Bias Field Correction.

X. Zhu, H. Pan, Y. Velichko, A. Murphy, A. Ross, B. Turkbey, A. Çetin, and U. Bagci. CoRR, (2024)

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Baris Yanik

Baris Ünver

Caroline Barisch

Baris Serifsoy

Imam Baris

Other publications of authors with the same name

Deep Learning Framework for Epithelium Density Estimation in Prostate Multi-Parametric Magnetic Resonance Imaging.M. To, S. Sankineni, S. Xu, B. Turkbey, P. Choyke, P. Pinto, V. Moreno, M. Merino, B. Wood, and J. Kwak. ISBI, page 438-441. IEEE, (2020)Multi-Task Learning for Registering Images With Large Deformation.B. Du, J. Liao, B. Turkbey, and P. Yan. IEEE J. Biomed. Health Informatics, 25 (5): 1624-1633 (2021)A collaborative computer aided diagnosis (C-CAD) system with eye-tracking, sparse attentional model, and deep learning.N. Khosravan, H. Celik, B. Turkbey, E. Jones, B. Wood, and U. Bagci. Medical Image Anal., (2019)Using YOLO v7 to Detect Kidney in Magnetic Resonance Imaging.P. Anari, F. Obiezu, N. Lay, F. Firouzabadi, A. Chaurasia, M. Golagha, S. Singh, F. Homayounieh, A. Zahergivar, S. Harmon and 9 other author(s). CoRR, (2024)Deeply-Supervised CNN for Prostate Segmentation.Q. Zhu, B. Du, B. Turkbey, P. Choyke, and P. Yan. CoRR, (2017)Realtime TRUS/MRI Fusion Targeted-Biopsy for Prostate Cancer: A Clinical Demonstration of Increased Positive Biopsy Rates.S. Kadoury, P. Yan, S. Xu, N. Glossop, P. Choyke, B. Turkbey, P. Pinto, B. Wood, and J. Kruecker. Prostate Cancer Imaging, volume 6367 of Lecture Notes in Computer Science, page 52-62. Springer, (2010)Atlas based AAM and SVM model for fully automatic MRI prostate segmentation.R. Cheng, B. Turkbey, W. Gandler, H. Agarwal, V. Shah, A. Bokinsky, E. McCreedy, S. Wang, S. Sankineni, M. Bernardo and 3 other author(s). EMBC, page 2881-2585. IEEE, (2014)Global structure constrained local shape prior estimation for medical image segmentation.P. Yan, W. Zhang, B. Turkbey, P. Choyke, and X. Li. Comput. Vis. Image Underst., 117 (9): 1017-1026 (2013)Discrete Deformable Model Guided by Partial Active Shape Model for TRUS Image Segmentation.P. Yan, S. Xu, B. Turkbey, and J. Kruecker. IEEE Trans. Biomed. Eng., 57 (5): 1158-1166 (2010)Active appearance model and deep learning for more accurate prostate segmentation on MRI.R. Cheng, H. Roth, L. Lu, S. Wang, B. Turkbey, W. Gandler, E. McCreedy, H. Agarwal, P. Choyke, R. Summers and 1 other author(s). Medical Imaging: Image Processing, volume 9784 of SPIE Proceedings, page 97842I. SPIE, (2016)

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Disambiguation of "Turkbey, Baris"

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A Probabilistic Hadamard U-Net for MRI Bias Field Correction.

Please choose a person to relate this publication to

Baris Yanik

Baris Ünver

Caroline Barisch

Baris Serifsoy

Imam Baris

Other publications of authors with the same name

Disambiguation

BibSonomy

Disambiguation of "Turkbey, Baris"

copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML A Probabilistic Hadamard U-Net for MRI Bias Field Correction.

Please choose a person to relate this publication to

Baris Yanik

Baris Ünver

Caroline Barisch

Baris Serifsoy

Imam Baris

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
Harvard
MSOffice XML

A Probabilistic Hadamard U-Net for MRI Bias Field Correction.