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Docs update: PLOS ONE publication README changes

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  1. README.md +16 -12
README.md CHANGED
@@ -5,13 +5,13 @@
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  <div align="center">
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  <h1 align="center">DDMR: Deep Deformation Map Registration</h1>
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- <h3 align="center">Train smarter, not harder: learning deep abdominal CT registration on scarce data</h3>
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  # ⚠️***WARNING: Under construction***
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- **DDMR** was developed by SINTEF Health Research. The corresponding manuscript describing the framework has been accepted for publication in [PLOS ONE](https://journals.plos.org/plosone/) and the preprint is openly available on [arXiv](https://arxiv.org/abs/2211.15717).
 
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-
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  </div>
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  ## 💻 Getting started
@@ -48,21 +48,25 @@ python EvaluationScripts/evaluation.py
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  ## ✨ How to cite
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  Please, consider citing our paper, if you find the work useful:
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  <pre>
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- @misc{perezdefrutos2022ddmr,
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- title = {Train smarter, not harder: learning deep abdominal CT registration on scarce data},
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- author = {Pérez de Frutos, Javier and Pedersen, André and Pelanis, Egidijus and Bouget, David and Survarachakan, Shanmugapriya and Langø, Thomas and Elle, Ole-Jakob and Lindseth, Frank},
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- year = {2022},
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- doi = {10.48550/ARXIV.2211.15717},
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- publisher = {arXiv},
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- copyright = {Creative Commons Attribution 4.0 International},
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- note = {preprint on arXiv at https://arxiv.org/abs/2211.15717}
 
 
 
 
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  }
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  </pre>
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  ## ⭐ Acknowledgements
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  This project is based on [VoxelMorph](https://github.com/voxelmorph/voxelmorph) library, and its related publication:
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  <pre>
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- @article{VoxelMorph2019,
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  title={VoxelMorph: A Learning Framework for Deformable Medical Image Registration},
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  author={Balakrishnan, Guha and Zhao, Amy and Sabuncu, Mert R. and Guttag, John and Dalca, Adrian V.},
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  journal={IEEE Transactions on Medical Imaging},
 
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  <div align="center">
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  <h1 align="center">DDMR: Deep Deformation Map Registration</h1>
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+ <h3 align="center">Learning deep abdominal CT registration through adaptive loss weighting and synthetic data generation</h3>
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  # ⚠️***WARNING: Under construction***
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+ **DDMR** was developed by SINTEF Health Research. The corresponding manuscript describing the framework has been published in [PLOS ONE](https://journals.plos.org/plosone/) and the manuscript is openly available [here](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0282110).
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+
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  </div>
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  ## 💻 Getting started
 
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  ## ✨ How to cite
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  Please, consider citing our paper, if you find the work useful:
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  <pre>
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+ @article{perezdefrutos2022ddmr,
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+ title = {Learning deep abdominal CT registration through adaptive loss weighting and synthetic data generation},
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+ author = {Pérez de Frutos, Javier AND Pedersen, André AND Pelanis, Egidijus AND Bouget, David AND Survarachakan, Shanmugapriya AND Langø, Thomas AND Elle, Ole-Jakob AND Lindseth, Frank},
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+ journal = {PLOS ONE},
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+ publisher = {Public Library of Science},
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+ year = {2023},
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+ month = {02},
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+ volume = {18},
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+ doi = {10.1371/journal.pone.0282110},
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+ url = {https://doi.org/10.1371/journal.pone.0282110},
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+ pages = {1-14},
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+ number = {2}
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  }
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  </pre>
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  ## ⭐ Acknowledgements
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  This project is based on [VoxelMorph](https://github.com/voxelmorph/voxelmorph) library, and its related publication:
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  <pre>
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+ @article{balakrishnan2019voxelmorph,
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  title={VoxelMorph: A Learning Framework for Deformable Medical Image Registration},
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  author={Balakrishnan, Guha and Zhao, Amy and Sabuncu, Mert R. and Guttag, John and Dalca, Adrian V.},
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  journal={IEEE Transactions on Medical Imaging},