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Improving the malignancy characterization of hepatocellular carcinoma using deeply supervised cross modal transfer learning for non-enhanced MR.

W. Jian, H. Ju, X. Cen, M. Cui, H. Zhang, L. Zhang, G. Wang, L. Gu, and W. Zhou. EMBC, page 853-856. IEEE, (2019)

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Zhang Zhang

Meng Zhang

Methods and implementations of road-network matchingM. Zhang. TU München, (2009)

Yue Zhang

Ning Zhang

Gang Zhang

Other publications of authors with the same name

Application of patient-reported outcomes in clinical evaluation of acupuncture for cervical spondylosis with artificial neural network.H. Wei, Z. Li, H. Zhang, Q. Chen, Z. hui Liang, and L. Chen. BIBM, page 271-276. IEEE Computer Society, (2013)Discrepancy Steered Conditional Adversarial Network For Deep Feature Based Malignancy Characterization of Hepatocellular Carcinoma.H. Ju, G. Wang, S. Men, H. Zhang, L. Gu, and W. Zhou. ICIP, page 1342-1345. IEEE, (2019)Super-Resolution and Self-Attention with Generative Adversarial Network for Improving Malignancy Characterization of Hepatocellular Carcinoma.Y. Li, H. Huang, L. Zhang, G. Wang, H. Zhang, and W. Zhou. ISBI, page 1556-1560. IEEE, (2020)Deriving mutual modes from HPLC fingerprints of traditional Chinese medicine with non-negative matrix factorization.H. Wei, L. Lin, Q. Chen, H. Zhang, S. dong Deng, and C. xia Liang. BIBM, page 265-270. IEEE Computer Society, (2013)Improving the malignancy characterization of hepatocellular carcinoma using deeply supervised cross modal transfer learning for non-enhanced MR.W. Jian, H. Ju, X. Cen, M. Cui, H. Zhang, L. Zhang, G. Wang, L. Gu, and W. Zhou. EMBC, page 853-856. IEEE, (2019)A clinical outcome evaluation model with local sample selection: A study on efficacy of acupuncture for cervical spondylosis.Z. Di, H. Zhang, G. Zhang, Z. Liang, L. Jiang, J. Liu, and W. Fu. BIBM Workshops, page 829-833. IEEE Computer Society, (2011)Automatic identification peaks in chromatographic fingerprints based on fuzzy matching.H. Wei, L. Lin, H. Zhang, and Q. Chen. FSKD, page 292-296. IEEE, (2012)Research on PRO scale of acupuncture for cervical spondylosis with multidimensional item response theory.X. Chen, H. Zhang, F. Lu, H. Wei, and Q. Chen. ICMLC, page 857-862. IEEE, (2016)Correlated and individual feature learning with contrast-enhanced MR for malignancy characterization of hepatocellular carcinoma.Y. Li, S. Li, H. Ju, T. Harada, H. Zhang, T. Duan, G. Wang, L. Zhang, L. Gu, and W. Zhou. Pattern Recognit., (October 2023)Patient Entity Recognition by Automatic EHR Context Understanding and Deep Learning.Z. Liang, J. Liu, H. Zhang, J. Huang, Z. Li, and S. Chan. BIBM, page 1096-1099. IEEE, (2019)

BibSonomy

Disambiguation of "Zhang, Honglai"

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Improving the malignancy characterization of hepatocellular carcinoma using deeply supervised cross modal transfer learning for non-enhanced MR.

Please choose a person to relate this publication to

Zhang Zhang

Meng Zhang

Yue Zhang

Ning Zhang

Gang Zhang

Other publications of authors with the same name

Disambiguation

BibSonomy

Disambiguation of "Zhang, Honglai"

copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Improving the malignancy characterization of hepatocellular carcinoma using deeply supervised cross modal transfer learning for non-enhanced MR.

Please choose a person to relate this publication to

Zhang Zhang

Meng Zhang

Yue Zhang

Ning Zhang

Gang Zhang

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

Improving the malignancy characterization of hepatocellular carcinoma using deeply supervised cross modal transfer learning for non-enhanced MR.