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@@ -9,6 +9,7 @@ tags:
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  - 3d-segmentation
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  library_name: nnunet
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  pipeline_tag: image-segmentation
 
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  ---
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  <p align="center">
@@ -137,13 +138,16 @@ GLOW-FDG was trained on 1,563 FDG-PET/CT scans pooled from public and institutio
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  ## Citation
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- If you use GLOW-FDG in your research, please cite:
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  ```bibtex
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- @article{fritsak2026glowfdg,
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- title = {GLOW-FDG: Generalized cancer LesiOn Whole-body segmentation model for 18F-FDG-PET/CT},
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- author = {Fritsak, Maksym and Rokuss, Maximilian and Maier-Hein, Klaus},
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- year = {2026}
 
 
 
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  }
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  @misc{rokuss2024fdgpsmahitchhikersguide,
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  }
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  ```
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  ## License
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  Released under the [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)](https://creativecommons.org/licenses/by-nc-sa/4.0/) license. The model weights may be used, shared, and adapted for **non-commercial** purposes with attribution; derivative works must be distributed under the same license. Note that some of the underlying training datasets carry their own licenses and data use agreements that may impose additional restrictions.
 
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  - 3d-segmentation
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  library_name: nnunet
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  pipeline_tag: image-segmentation
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+ thumbnail: GLOW-FDG/logo.jpg
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  ---
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  <p align="center">
 
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  ## Citation
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+ If you use GLOW-FDG in your research, please cite the related evaluation study and the methodological precursor:
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  ```bibtex
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+ @article{fritsak2026generalizing,
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+ title = {Generalizing Beyond Training Data: Performance Assessment of the Best AutoPET III Model on Diverse Cancer Types},
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+ author = {Fritsak, Maksym and Gabrys, Hubert and Rokuss, Maximilian and Christ, Sebastian and Martz, Nicolas and Paunoiu, Alina and Opitz, Isabelle and Stahel, Rolf A. and Huellner, Martin W. and Guckenberger, Matthias and Tanadini-Lang, Stephanie},
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+ year = {2026},
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+ note = {Available at SSRN},
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+ doi = {10.2139/ssrn.6748676},
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+ url = {https://ssrn.com/abstract=6748676}
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  }
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  @misc{rokuss2024fdgpsmahitchhikersguide,
 
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  }
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  ```
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+ A dedicated GLOW-FDG manuscript is in preparation; this section will be updated once it becomes publicly available.
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+
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  ## License
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  Released under the [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)](https://creativecommons.org/licenses/by-nc-sa/4.0/) license. The model weights may be used, shared, and adapted for **non-commercial** purposes with attribution; derivative works must be distributed under the same license. Note that some of the underlying training datasets carry their own licenses and data use agreements that may impose additional restrictions.