Author of the publication

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

Reler: Relearning Controversial Regions to Accurately Segment Nasopharyngeal Carcinoma.

G. Tao, H. Li, D. Lu, Z. Ling, L. Liu, and H. Cai. BIBM, page 1207-1212. IEEE, (2022)

Please choose a person to relate this publication to

To differ between persons with the same name, the academic degree and the title of an important publication will be displayed. You can also use the button next to the name to display some publications already assigned to the person.

Li Li

Other publications of authors with the same name

Detection-and-Excitation Neural Network Achieves Accurate Nasopharyngeal Carcinoma Segmentation in Multi-modality MR Images.G. Tao, H. Li, L. Liu, and H. Cai. BIBM, page 1063-1068. IEEE, (2021)LD-UNet: A long-distance perceptual model for segmentation of blurred boundaries in medical images.S. Chen, C. Luo, S. Liu, H. Li, Y. Liu, H. Zhou, L. Liu, and H. Chen. Comput. Biol. Medicine, (2024)Time-to-Event Supervised Genetic Algorithm Enables Induction Chemotherapy Decision Making for Nasopharyngeal Carcinoma.D. Liu, H. Li, L. Wu, S. Chen, T. Zhang, W. Huang, G. Ruan, S. Li, L. Liu, and H. Chen. IEEE Access, (2021)Achieving Accurate Segmentation of Nasopharyngeal Carcinoma in MR Images Through Recurrent Attention.J. bin Huang, E. Zhuo, H. Li, L. Liu, H. Cai, and Y. Ou. MICCAI (5), volume 11768 of Lecture Notes in Computer Science, page 494-502. Springer, (2019)U-Net Plus: Deep Semantic Segmentation for Esophagus and Esophageal Cancer in Computed Tomography Images.S. Chen, H. Yang, J. Fu, W. Mei, S. Ren, Y. Liu, Z. Zhu, L. Liu, H. Li, and H. Chen. IEEE Access, (2019)Reler: Relearning Controversial Regions to Accurately Segment Nasopharyngeal Carcinoma.G. Tao, H. Li, D. Lu, Z. Ling, L. Liu, and H. Cai. BIBM, page 1207-1212. IEEE, (2022)NPCNet: Jointly Segment Primary Nasopharyngeal Carcinoma Tumors and Metastatic Lymph Nodes in MR Images.Y. Li, T. Dan, H. Li, J. Chen, H. Peng, L. Liu, and H. Cai. IEEE Trans. Medical Imaging, 41 (7): 1639-1650 (2022)Channel-Attention U-Net: Channel Attention Mechanism for Semantic Segmentation of Esophagus and Esophageal Cancer.G. Huang, J. Zhu, J. Li, Z. Wang, L. Cheng, L. Liu, H. Li, and J. Zhou. IEEE Access, (2020)Anatomical Point-of-Interest Detection in Head MRI Using Multipoint Feature Descriptor.S. Li, S. Chen, H. Li, G. Ruan, S. Ren, T. Zhang, L. Liu, and H. Chen. IEEE Access, (2020)SeqSeg: A sequential method to achieve nasopharyngeal carcinoma segmentation free from background dominance.G. Tao, H. Li, J. Huang, C. Han, J. Chen, G. Ruan, W. Huang, Y. Hu, T. Dan, B. Zhang and 3 other author(s). Medical Image Anal., (2022)

BibSonomy

Disambiguation of "Li, Haojiang"

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

Reler: Relearning Controversial Regions to Accurately Segment Nasopharyngeal Carcinoma.

Please choose a person to relate this publication to

Li Li

Li Li

Li Li

Li Li

Li Li

Other publications of authors with the same name

Disambiguation

BibSonomy

Disambiguation of "Li, Haojiang"

copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Reler: Relearning Controversial Regions to Accurately Segment Nasopharyngeal Carcinoma.

Please choose a person to relate this publication to

Li Li

Li Li

Li Li

Li Li

Li Li

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

Reler: Relearning Controversial Regions to Accurately Segment Nasopharyngeal Carcinoma.