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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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Xu Xu

Xianxiang Xu

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

Yan Xu

Untersuchungen zur Verleimung von Holz und Holzspanplatten mit UF-Leimharzen und PMDIY. Xu. TU Braunschweig, (2009)

Dong Xu

Other publications of authors with the same name

Interstitial Lung Diseases via Deep Convolutional Neural Networks: Segmentation Label Propagation, Unordered Pooling and Cross-Dataset Learning.M. Gao, Z. Xu, and D. Mollura. Deep Learning and Convolutional Neural Networks for Medical Image Computing, Springer, (2017)A hybrid multi-scale approach to automatic airway tree segmentation from CT scans.Z. Xu, U. Bagci, B. Foster, and D. Mollura. ISBI, page 1308-1311. IEEE, (2013)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)CIDI-lung-seg: A single-click annotation tool for automatic delineation of lungs from CT scans.A. Mansoor, U. Bagci, B. Foster, Z. Xu, D. Douglas, J. Solomon, J. Udupa, and D. Mollura. EMBC, page 1087-1090. IEEE, (2014)Progressive and Multi-path Holistically Nested Neural Networks for Pathological Lung Segmentation from CT Images.A. Harrison, Z. Xu, K. George, L. Lu, R. Summers, and D. Mollura. MICCAI (3), volume 10435 of Lecture Notes in Computer Science, page 621-629. Springer, (2017)Characterization of Lung Nodule Malignancy Using Hybrid Shape and Appearance Features.M. Buty, Z. Xu, M. Gao, U. Bagci, A. Wu, and D. Mollura. MICCAI (1), volume 9900 of Lecture Notes in Computer Science, page 662-670. (2016)Information Bottleneck Attribution for Visual Explanations of Diagnosis and Prognosis.U. Demir, I. Irmakci, E. Keles, A. Topcu, Z. Xu, C. Spampinato, S. Jambawalikar, E. Turkbey, B. Turkbey, and U. Bagci. MLMI@MICCAI, volume 12966 of Lecture Notes in Computer Science, page 396-405. Springer, (2021)A 30% Efficient High-Output Voltage Fully Integrated Self-Biased Gate RF Rectifier Topology for Neural Implants.Z. Xu, A. Khalifa, A. Mittal, M. Nasrollahpourmotlaghzanjani, D. Das, M. Onabajo, N. Sun, S. Cash, and A. Shrivastava. IEEE J. Solid State Circuits, 57 (11): 3324-3335 (2022)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)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)

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Disambiguation of "Xu, Ziyue"

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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

Xu Xu

Xu Xu

Xianxiang Xu

Yan Xu

Dong Xu

Other publications of authors with the same name

Disambiguation

BibSonomy

Disambiguation of "Xu, Ziyue"

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

Xu Xu

Xu Xu

Xianxiang Xu

Yan Xu

Dong Xu

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