Toward Accurate Cardiac MRI Segmentation With Variational Autoencoder-Based Unsupervised Domain Adaptation-Reference-Cited by-同舟云学术

Toward Accurate Cardiac MRI Segmentation With Variational Autoencoder-Based Unsupervised Domain Adaptation

Published:2024-08 Issue:8 Volume:43 Page:2924-2936
ISSN:0278-0062
Container-title:IEEE Transactions on Medical Imaging
language:
Short-container-title:IEEE Trans. Med. Imaging

Author:

Cui Hengfei¹^ORCID,Li Yan¹^ORCID,Wang Yifan¹,Xu Di²^ORCID,Wu Lian-Ming³^ORCID,Xia Yong¹^ORCID

Affiliation:

1. National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology, School of Computer Science, Northwestern Polytechnical University, Xi’an, China

2. Media Innovation Laboratory, Huawei Cloud, Xi’an, China

3. Department of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China

Funder

National Natural Science Foundation of China

Basic and Applied Basic Research Foundation of Guangdong Province

Natural Science Foundation of Chongqing, China

Key Research and Development Program of Shaanxi Province, China

Shenzhen Science and Technology Program

Shanghai Municipal Commission of Science and Technology Medical Innovation Research Special Project

Shanghai “Yiyuan New Star” Outstanding Youth Talent

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/42/10619995/10483021.pdf?arnumber=10483021

Reference38 articles.

1. U-Net: Convolutional Networks for Biomedical Image Segmentation

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3. Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images

4. FocalMix: Semi-Supervised Learning for 3D Medical Image Detection

5. An Adversarial Learning Approach to Medical Image Synthesis for Lesion Detection

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