Reconstruction of Partially Broken Vascular Structures in X-Ray Images via Vesselness-Loss-Based Multi-Scale Generative Adversarial Networks-Reference-Cited by-同舟云学术

Reconstruction of Partially Broken Vascular Structures in X-Ray Images via Vesselness-Loss-Based Multi-Scale Generative Adversarial Networks

Published:2023 Issue: Volume:11 Page:86335-86350
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Han Kyunghoon¹^ORCID,Koo Heejoon²,Jung Sunghee³^ORCID,Park Hyung-Bok²^ORCID,Hong Youngtaek²^ORCID,Shim Hackjoon²^ORCID,Jeon Byunghwan⁴^ORCID,Chang Hyuk-Jae²^ORCID

Affiliation:

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

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

3. School of Electrical Engineering, Kookmin University, Seoul, South Korea

4. Division of Computer Engineering, Hankuk University of Foreign Studies, Yongin-si, South Korea

Funder

Korea Medical Device Development Fund

Korean Government (the Ministry of Science and ICT

Hankuk University of Foreign Studies Research Fund of 2023

MSIT, Korea, through the National Program for Excellence in SW

Institute of Information and Communications Technology Planning and Evaluation (IITP) in 2023

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/10005208/10207016.pdf?arnumber=10207016

Reference55 articles.

1. A Style-Based Generator Architecture for Generative Adversarial Networks

2. Generative adversarial nets;goodfellow;Proc Adv Neural Inf Process Syst,2014

3. Context Encoders: Feature Learning by Inpainting

4. Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks

5. Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network