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O2M-UDA: Unsupervised dynamic domain adaptation for one-to-multiple medical image segmentation.

Z. Jiang, Y. He, S. Ye, P. Shao, X. Zhu, Y. Xu, Y. Chen, J. Coatrieux, S. Li, and G. Yang. Knowl. Based Syst., (April 2023)

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Guanyu Wang

Yang Yang

Other publications of authors with the same name

Efficient Legendre moment computation for grey level images.G. Yang, H. Shu, C. Toumoulin, G. Han, and L. Luo. CoRR, (2014)PV-LVNet: Direct left ventricle multitype indices estimation from 2D echocardiograms of paired apical views with deep neural networks.R. Ge, G. Yang, Y. Chen, L. Luo, C. Feng, H. Zhang, and S. Li. Medical Image Anal., (2019)Motion Compensated Tomography Reconstruction of Coronary Arteries in Rotational Angiography.A. Bousse, J. Zhou, G. Yang, J. Bellanger, and C. Toumoulin. IEEE Trans. Biomed. Eng., 56 (4): 1254-1257 (2009)An Improved Real-Time Endovascular Guidewire Position Simulation Using Activity on Edge Network.J. Qiu, L. Zhang, G. Yang, Y. Chen, and S. Zhou. IEEE Access, (2019)Left Ventricle Full Quantification via Hierarchical Quantification Network.G. Yang, T. Hua, C. Lu, T. Pan, X. Yang, L. Hu, J. Wu, X. Zhu, and H. Shu. STACOM@MICCAI, volume 11395 of Lecture Notes in Computer Science, page 429-438. Springer, (2018)HIFUNet: Multi-Class Segmentation of Uterine Regions From MR Images Using Global Convolutional Networks for HIFU Surgery Planning.C. Zhang, H. Shu, G. Yang, F. Li, Y. Wen, Q. Zhang, J. Dillenseger, and J. Coatrieux. IEEE Trans. Medical Imaging, 39 (11): 3309-3320 (2020)FedSODA: Federated Cross-assessment and Dynamic Aggregation for Histopathology Segmentation.Y. Zhang, Y. Qi, X. Qi, L. Senhadji, Y. Wei, F. Chen, and G. Yang. CoRR, (2023)Left Ventricle Quantification Challenge: A Comprehensive Comparison and Evaluation of Segmentation and Regression for Mid-Ventricular Short-Axis Cardiac MR Data.W. Xue, J. Li, Z. Hu, E. Kerfoot, J. Clough, I. Öksüz, H. Xu, V. Grau, F. Guo, M. Ng and 15 other author(s). IEEE J. Biomed. Health Informatics, 25 (9): 3541-3553 (2021)STANet: Spatio-Temporal Adaptive Network and Clinical Prior Embedding Learning for 3D+T CMR Segmentation.X. Qi, Y. He, Y. Qi, Y. Kong, G. Yang, and S. Li. IEEE J. Biomed. Health Informatics, 28 (2): 881-892 (February 2024)Learning Better Registration to Learn Better Few-Shot Medical Image Segmentation: Authenticity, Diversity, and Robustness.Y. He, R. Ge, X. Qi, Y. Chen, J. Wu, J. Coatrieux, G. Yang, and S. Li. IEEE Trans. Neural Networks Learn. Syst., 35 (2): 2588-2601 (February 2024)

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Disambiguation of "Yang, Guanyu"

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O2M-UDA: Unsupervised dynamic domain adaptation for one-to-multiple medical image segmentation.

Please choose a person to relate this publication to

Guanyu Wang

Yang Yang

Yang Yang

Yang Yang

Yang Yang

Other publications of authors with the same name

Disambiguation

BibSonomy

Disambiguation of "Yang, Guanyu"

copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML O2M-UDA: Unsupervised dynamic domain adaptation for one-to-multiple medical image segmentation.

Please choose a person to relate this publication to

Guanyu Wang

Yang Yang

Yang Yang

Yang Yang

Yang Yang

Other publications of authors with the same name

Disambiguation

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URL
DOI
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Chicago
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O2M-UDA: Unsupervised dynamic domain adaptation for one-to-multiple medical image segmentation.