Weakly-supervised segmentation using inherently-explainable classification models

Official code of the paper "Weakly-supervised segmentation using inherently-explainable classification models and their application to brain tumour classification" (https://arxiv.org/abs/2206.05148).

This was first presented at ISMRM-ESMRMB 2022, London. Abstract available on RG: https://www.researchgate.net/publication/358357555_Learning_to_segment_brain_tumours_using_an_explainable_classifier

Contacts

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soumick.chatterjee@ovgu.de

contact@soumick.com

Credits

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If you use this approach in your research or use codes from this repository, please cite either or both of the following in your publications:

The complete manuscript on ArXiv:-

Soumick Chatterjee, Hadya Yassin, Florian Dubost, Andreas Nürnberger, Oliver Speck: Weakly-supervised segmentation using inherently-explainable classification models and their application to brain tumour classification (arXiv:2206.05148, Jun 2022)

BibTeX entry:

@article{chatterjee2022micdir,
  title={MICDIR: Multi-scale Inverse-consistent Deformable Image Registration using UNetMSS with Self-Constructing Graph Latent},
  author={Chatterjee, Soumick and Hadya, Yassin and Dubost, Florian and N{\"u}rnberger, Andreas} and Speck, Oliver,
  journal={arXiv preprint arXiv:2206.05148},
  year={2022}
}

The ISMRM-ESMRMB 2022 abstract:-

Soumick Chatterjee, Hadya Yassin, Florian Dubost, Andreas Nürnberger, Oliver Speck: Learning to segment brain tumours using an explainable classifier (ISMRM-ESMRMB 2022, May 2022)

BibTeX entry:

@inproceedings{mickISMRM22gp,
      author = {Chatterjee, Soumick and Yassin, Hadya and Dubost, Florian and Nürnberger, Andreas and Speck, Oliver},
      year = {2022},
      month = {05},
      pages = {0171},
      title = {Learning to segment brain tumours using an explainable classifier},
      booktitle={ISMRM-ESMRMB 2022}
}

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