Local Style Transfer via Latent Space Manipulation for Cross-Disease Lesion Segmentation-Reference-Cited by-同舟云学术

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Local Style Transfer via Latent Space Manipulation for Cross-Disease Lesion Segmentation

Published:2024-01 Issue:1 Volume:28 Page:273-284
ISSN:2168-2194
Container-title:IEEE Journal of Biomedical and Health Informatics
language:
Short-container-title:IEEE J. Biomed. Health Inform.

Author:

Lyu Fei¹^ORCID,Ye Mang²^ORCID,Yip Terry Cheuk-Fung³^ORCID,Wong Grace Lai-Hung³^ORCID,Yuen Pong C.¹^ORCID

Affiliation:

1. Department of Computer Science, Hong Kong Baptist University, Hong Kong

2. School of Computer Science, Wuhan University, Wuhan, China

3. Medical Data Analytics Centre, Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong

Funder

Hong Kong Research Grants Council General Research Fund

Key Research and Development Project of Hubei Province

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Health Information Management,Electrical and Electronic Engineering,Computer Science Applications,Health Informatics

Link

http://xplorestaging.ieee.org/ielx7/6221020/10380963/10297423.pdf?arnumber=10297423

Reference56 articles.

1. Deep learning-based triage and analysis of lesion burden for COVID-19: a retrospective study with external validation

2. A Mutual Bootstrapping Model for Automated Skin Lesion Segmentation and Classification

3. Label-Free Liver Tumor Segmentation

4. A Novel Coronavirus from Patients with Pneumonia in China, 2019

5. Self-Ensembling Co-Training Framework for Semi-Supervised COVID-19 CT Segmentation

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