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Disruptive Autoencoders: Leveraging Low-level features for 3D Medical Image Pre-training.

J. Valanarasu, Y. Tang, D. Yang, Z. Xu, C. Zhao, W. Li, V. Patel, B. Landman, D. Xu, Y. He, and V. Nath. CoRR, (2023)

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Daguang Han

Daguang Cai

Xu Xu

Xianxiang Xu

Imaging findings of cerebral schistosomiasis in ChinaX. Xu. Uni Hamburg, (2010)

Other publications of authors with the same name

Closing the Generalization Gap of Cross-silo Federated Medical Image Segmentation.A. Xu, W. Li, P. Guo, D. Yang, H. Roth, A. Hatamizadeh, C. Zhao, D. Xu, H. Huang, and Z. Xu. CVPR, page 20834-20843. IEEE, (2022)V-NAS: Neural Architecture Search for Volumetric Medical Image Segmentation.Z. Zhu, C. Liu, D. Yang, A. Yuille, and D. Xu. 3DV, page 240-248. IEEE, (2019)Federated Learning for Breast Density Classification: A Real-World Implementation.H. Roth, K. Chang, P. Singh, N. Neumark, W. Li, V. Gupta, S. Gupta, L. Qu, A. Ihsani, B. Bizzo and 33 other author(s). DART/DCL@MICCAI, volume 12444 of Lecture Notes in Computer Science, page 181-191. Springer, (2020)4D CNN for Semantic Segmentation of Cardiac Volumetric Sequences.A. Myronenko, D. Yang, V. Buch, D. Xu, A. Ihsani, S. Doyle, M. Michalski, N. Tenenholtz, and H. Roth. STACOM@MICCAI, volume 12009 of Lecture Notes in Computer Science, page 72-80. Springer, (2019)DeepEdit: Deep Editable Learning for Interactive Segmentation of 3D Medical Images.A. Diaz-Pinto, P. Mehta, S. Alle, M. Asad, R. Brown, V. Nath, A. Ihsani, M. Antonelli, D. Palkovics, C. Pinter and 10 other author(s). DALI@MICCAI, volume 13567 of Lecture Notes in Computer Science, page 11-21. Springer, (2022)VerSe: A Vertebrae labelling and segmentation benchmark for multi-detector CT images.A. Sekuboyina, M. Husseini, A. Bayat, M. Löffler, H. Liebl, H. Li, G. Tetteh, J. Kukacka, C. Payer, D. Stern and 59 other author(s). Medical Image Anal., (2021)Multi-task Federated Learning for Heterogeneous Pancreas Segmentation.C. Shen, P. Wang, H. Roth, D. Yang, D. Xu, M. Oda, W. Wang, C. Fuh, P. Chen, K. Liu and 2 other author(s). CLIP/DCL/LL-COVID19/PPML@MICCAI, volume 12969 of Lecture Notes in Computer Science, page 101-110. Springer, (2021)Disruptive Autoencoders: Leveraging Low-level features for 3D Medical Image Pre-training.J. Valanarasu, Y. Tang, D. Yang, Z. Xu, C. Zhao, W. Li, V. Patel, B. Landman, D. Xu, Y. He and 1 other author(s). CoRR, (2023)DAST: Differentiable Architecture Search with Transformer for 3D Medical Image Segmentation.D. Yang, Z. Xu, Y. He, V. Nath, W. Li, A. Myronenko, A. Hatamizadeh, C. Zhao, H. Roth, and D. Xu. MICCAI (3), volume 14222 of Lecture Notes in Computer Science, page 747-756. Springer, (2023)SwinUNETR-V2: Stronger Swin Transformers with Stagewise Convolutions for 3D Medical Image Segmentation.Y. He, V. Nath, D. Yang, Y. Tang, A. Myronenko, and D. Xu. MICCAI (4), volume 14223 of Lecture Notes in Computer Science, page 416-426. Springer, (2023)

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Disambiguation of "Xu, Daguang"

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Disruptive Autoencoders: Leveraging Low-level features for 3D Medical Image Pre-training.

Please choose a person to relate this publication to

Daguang Han

Daguang Cai

Xu Xu

Xu Xu

Xianxiang Xu

Other publications of authors with the same name

Disambiguation

BibSonomy

Disambiguation of "Xu, Daguang"

copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Disruptive Autoencoders: Leveraging Low-level features for 3D Medical Image Pre-training.

Please choose a person to relate this publication to

Daguang Han

Daguang Cai

Xu Xu

Xu Xu

Xianxiang Xu

Other publications of authors with the same name

Disambiguation

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URL
DOI
BibTeX
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Disruptive Autoencoders: Leveraging Low-level features for 3D Medical Image Pre-training.