Improving the Reproducibility of Computed Tomography Radiomic Features Using an Enhanced Hierarchical Feature Synthesis Network-Reference-Cited by-同舟云学术

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Improving the Reproducibility of Computed Tomography Radiomic Features Using an Enhanced Hierarchical Feature Synthesis Network

Published:2024 Issue: Volume:12 Page:27648-27660
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Jeong Dawun¹^ORCID,Hong Youngtaek²^ORCID,Lee Jina¹,Lee Seul Bi³,Cho Yeon Jin³^ORCID,Shim Hackjoon²^ORCID,Chang Hyuk-Jae²^ORCID

Affiliation:

1. Brain Korea 21 Project, Graduate School of Medical Science, Yonsei University College of Medicine, Seoul, South Korea

2. CONNECT-AI Research Center, Yonsei University College of Medicine, Seoul, South Korea

3. Department of Radiology, Seoul National University Hospital, Seoul, Jongno-gu, Republic of Korea

Funder

Korean Government (the Ministry of Science and Information and Communication Technology

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6287639/10380310/10439178.pdf?arnumber=10439178

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. Improved microvascular imaging with optical coherence tomography using 3D neural networks and a channel attention mechanism;Scientific Reports;2024-08-01

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