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Cited by 30Medical Image Segmentation TechniquesDomain Adaptation and Few-Shot LearningBrain Tumor Detection and Classification

An unsupervised domain adaptation brain CT segmentation method across image modalities and diseases

Open access
Dong, Daqiang, Fu, Guanghui, Li, Jianqiang, Pei, Yan, Chen, Yueda·2022·Expert Systems with Applications
DOI 10.1016/j.eswa.2022.118016
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  • CNN has been widely applied in various medical imaging, laboratory reports, pathology reports, etc., and has achieved remarkable success in the field of AI-based diagnosis.

    “Recently, the CNN has been widely applied in various medical imaging, laboratory reports, pathology reports, etc., and has achieved remarkable success in the field of AI-based diagnosis [51,52,53,54,55,56,57,58].”

    From A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis (2024) · cites this paper as [51]

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References 71 · Cited by 30

Authors
Dong, D., Fu, G., Li, J., Pei, Y., & Chen, Y.
Affiliations
  • Beijing University of Technology (CN)
  • Centre National de la Recherche Scientifique (FR)
  • University of Aizu (JP)
  • Tianjin Huanhu Hospital (CN)
Corresponding author
Li, Jianqiang
ORCID
Venue
Expert Systems with Applications
Publisher
Elsevier BV
Published
2022-11
Volume
207
Pages
118016
ISSN
0957-4174 (ISSN-L)
DOI
10.1016/j.eswa.2022.118016
OpenAlex
W4285081805
Cited by
30
References
71
Citations by year
2026: 5 · 2025: 6 · 2024: 12 · 2023: 7
FWCI
2.04
Type
journal article
Language
en
Open access
Licence
Locations
  • Expert Systems with Applications (publishedVersion)
  • HAL (submittedVersion, other-oa)
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
  • Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
  • Life Sciences › Neuroscience › Neurology › Brain Tumor Detection and Classification
Keywords
unsupervised domain adaptation, intraparenchymal hemorrhage, hemorrhage segmentation, MRI-to-CT translation, CycleGAN, glioma, cross-modality segmentation, label-free segmentation

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