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

Efficient and Robust Instrument Segmentation in 3D Ultrasound Using Patch-of-Interest-FuseNet with Hybrid Loss.

H. Yang, C. Shan, R. Bouwman, A. Kolen, and P. de With. Medical Image Anal., (2021)

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.

Yang Yang

Other publications of authors with the same name

Efficient Catheter Segmentation in 3D Cardiac Ultrasound using Slice-Based FCN With Deep Supervision and F-Score Loss.H. Yang, C. Shan, A. Kolen, and P. de With. ICIP, page 260-264. IEEE, (2019)Medical Instrument Detection in Ultrasound-Guided Interventions: A Review.H. Yang, C. Shan, A. Kolen, and P. de With. CoRR, (2020)Catheter localization in 3D ultrasound using voxel-of-interest-based ConvNets for cardiac intervention.H. Yang, C. Shan, A. Kolen, and P. de With. Int. J. Comput. Assist. Radiol. Surg., 14 (6): 1069-1077 (2019)Medical Instrument Segmentation in 3D US by Hybrid Constrained Semi-Supervised Learning.H. Yang, C. Shan, R. Bouwman, L. Dekker, A. Kolen, and P. de With. IEEE J. Biomed. Health Informatics, 26 (2): 762-773 (2022)A unified distributed ELM framework with supervised, semi-supervised and unsupervised big data learning.Z. Wang, L. Qu, J. Xin, H. Yang, and X. Gao. Memetic Computing, 11 (3): 305-315 (2019)Improving Catheter Segmentation & Localization in 3D Cardiac Ultrasound Using Direction-Fused Fcn.H. Yang, C. Shan, A. Kolen, and P. de With. ISBI, page 1122-1126. IEEE, (2019)Weakly-supervised learning for catheter segmentation in 3D frustum ultrasound.H. Yang, C. Shan, A. Kolen, and P. de With. Comput. Medical Imaging Graph., (2022)Multi-modal trained artificial intelligence solution to triage chest X-ray for COVID-19 using pristine ground-truth, versus radiologists.T. Tan, B. Das, R. Soni, M. Fejes, H. Yang, S. Ranjan, D. Szabo, V. Melapudi, K. Shriram, U. Agrawal and 7 other author(s). Neurocomputing, (2022)Medical Instrument Segmentation in 3D US by Hybrid Constrained Semi-Supervised Learning.H. Yang, C. Shan, R. Bouwman, L. Dekker, A. Kolen, and P. de With. CoRR, (2021)Deep Q-Network-Driven Catheter Segmentation in 3D US by Hybrid Constrained Semi-supervised Learning and Dual-UNet.H. Yang, C. Shan, A. Kolen, and P. de With. MICCAI (1), volume 12261 of Lecture Notes in Computer Science, page 646-655. Springer, (2020)

BibSonomy

Disambiguation of "Yang, Hongxu"

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

Efficient and Robust Instrument Segmentation in 3D Ultrasound Using Patch-of-Interest-FuseNet with Hybrid Loss.

Please choose a person to relate this publication to

Yang Yang

Yang Yang

Yang Yang

Yang Yang

Yang Yang

Other publications of authors with the same name

Disambiguation

BibSonomy

Disambiguation of "Yang, Hongxu"

copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Efficient and Robust Instrument Segmentation in 3D Ultrasound Using Patch-of-Interest-FuseNet with Hybrid Loss.

Please choose a person to relate this publication to

Yang Yang

Yang Yang

Yang Yang

Yang Yang

Yang Yang

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

Efficient and Robust Instrument Segmentation in 3D Ultrasound Using Patch-of-Interest-FuseNet with Hybrid Loss.