Multi-task semantic segmentation of CT images for COVID-19 infections using DeepLabV3+ based on dilated residual network

Hasan Polat

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DOI: 10.1007/s13246-022-01110-w
Published: 2022-03-14
Container: Physical and Engineering Sciences in Medicine
Publisher: Springer Science and Business Media LLC
Open access: unknown

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Cite this work

BibTeX

@article{allodium:10.1007/s13246-022-01110-w,
  title = {Multi-task semantic segmentation of CT images for COVID-19 infections using DeepLabV3+ based on dilated residual network},
  author = {Hasan Polat},
  year = {2022},
  journal = {Physical and Engineering Sciences in Medicine},
  doi = {10.1007/s13246-022-01110-w},
  url = {https://doi.org/10.1007/s13246-022-01110-w}
}

RIS

TY  - JOUR
TI  - Multi-task semantic segmentation of CT images for COVID-19 infections using DeepLabV3+ based on dilated residual network
AU  - Hasan Polat
PY  - 2022
JO  - Physical and Engineering Sciences in Medicine
DO  - 10.1007/s13246-022-01110-w
UR  - https://doi.org/10.1007/s13246-022-01110-w
ER  -

APA

Polat, H. (2022). Multi-task semantic segmentation of CT images for COVID-19 infections using DeepLabV3+ based on dilated residual network. Physical and Engineering Sciences in Medicine. https://doi.org/10.1007/s13246-022-01110-w

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crossref · retrieved 2026-09-27T12:25:32.035Z