Learning Without Real Data Annotations to Detect Hepatic Lesions in PET Images-Reference-Cited by-同舟云学术

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Learning Without Real Data Annotations to Detect Hepatic Lesions in PET Images

Published:2024-02 Issue:2 Volume:71 Page:679-688
ISSN:0018-9294
Container-title:IEEE Transactions on Biomedical Engineering
language:
Short-container-title:IEEE Trans. Biomed. Eng.

Author:

Yang Xinyi¹^ORCID,Chin Bennett B.²^ORCID,Silosky Michael²^ORCID,Wehrend Jonathan³,Litwiller Daniel V.⁴,Ghosh Debashis¹^ORCID,Xing Fuyong⁵^ORCID

Affiliation:

1. Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus, USA

2. Department of Radiology, University of Colorado Anschutz Medical Campus, USA

3. Department of Radiology, Santa Clara Valley Medical Center, USA

4. GE Healthcare, USA

5. Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA

Funder

University of Colorado

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Biomedical Engineering

Link

http://xplorestaging.ieee.org/ielx7/10/10410108/10251534.pdf?arnumber=10251534

Reference79 articles.

1. Nothing But NET: A Review of Neuroendocrine Tumors and Carcinomas

2. Neuroendocrine Tumor Diagnosis and Management:68Ga-DOTATATE PET/CT

3. A Comparison of 68Ga-DOTATATE and 18F-FDG PET/CT in Pulmonary Neuroendocrine Tumors

4. Classification and evaluation strategies of auto-segmentation approaches for PET: Report of AAPM task group No. 211

5. The first MICCAI challenge on PET tumor segmentation

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Improving Generalizability of PET DL Algorithms: List-Mode Reconstructions Improve DOTATATE PET Hepatic Lesion Detection Performance;Bioengineering;2024-02-27

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