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Cited by 2,273Brain Tumor Detection and ClassificationMedical Image Segmentation TechniquesAdvanced Neural Network Applications

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

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Hatamizadeh, Ali, Nath, Vishwesh, Tang, Yucheng, Yang, Dong, Roth, Holger R., Xu, Daguang·2022·Lecture Notes in Computer Science
DOI 10.1007/978-3-031-08999-2_22
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Authors
Hatamizadeh, A., Nath, V., Tang, Y., Yang, D., Roth, H. R., & Xu, D.
Affiliations
  • Nvidia (United States) (US)
  • Vanderbilt University (US)
Corresponding author
Hatamizadeh, Ali
ORCID
Venue
Lecture Notes in Computer Science
Publisher
Springer International Publishing
Published
2022
Pages
272-284
ISSN
  • 0302-9743 (ISSN-L)
  • 1611-3349
DOI
10.1007/978-3-031-08999-2_22
OpenAlex
W4221163766
Cited by
2,273
References
8
Citations by year
2026: 611 · 2025: 828 · 2024: 561 · 2023: 234 · 2022: 39
FWCI
260.97
Type
conference paper
Language
en
Open access
Licence
Locations
  • Lecture notes in computer science (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University)
Topics
  • Life Sciences › Neuroscience › Neurology › Brain Tumor Detection and Classification
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
Keywords
brain tumor segmentation, semantic segmentation, Swin UNETR, Swin Transformer, 3D medical image segmentation, magnetic resonance imaging, multimodal imaging, long-range dependencies, self-attention, sequence-to-sequence prediction, BraTS 2021, U-Net decoder

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