Semi-SwinUNeTR: Towards 3D Swin Vision Transformer-Based UNet for Medical Image Segmentation with Limited Annotations

Yinbing Tian, Ziyang Wang, Li Guo

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DOI: 10.3390/bioengineering13060695
Published: 2026-06-17
Container: Bioengineering
Publisher: MDPI AG
Open access: unknown

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Cite this work

BibTeX

@article{allodium:10.3390/bioengineering13060695,
  title = {Semi-SwinUNeTR: Towards 3D Swin Vision Transformer-Based UNet for Medical Image Segmentation with Limited Annotations},
  author = {Yinbing Tian and Ziyang Wang and Li Guo},
  year = {2026},
  journal = {Bioengineering},
  doi = {10.3390/bioengineering13060695},
  url = {https://doi.org/10.3390/bioengineering13060695}
}

RIS

TY  - JOUR
TI  - Semi-SwinUNeTR: Towards 3D Swin Vision Transformer-Based UNet for Medical Image Segmentation with Limited Annotations
AU  - Yinbing Tian
AU  - Ziyang Wang
AU  - Li Guo
PY  - 2026
JO  - Bioengineering
DO  - 10.3390/bioengineering13060695
UR  - https://doi.org/10.3390/bioengineering13060695
ER  -

APA

Tian, Y., Wang, Z., & Guo, L. (2026). Semi-SwinUNeTR: Towards 3D Swin Vision Transformer-Based UNet for Medical Image Segmentation with Limited Annotations. Bioengineering. https://doi.org/10.3390/bioengineering13060695

Source records

crossref · retrieved 2026-11-10T11:53:42.538Z