PLISM dataset

This preprocessed dataset was directly generated from owkin/plism-dataset-tiles. It is meant to perform the features extraction in a more convenient way. As such, this dataset contains 91 .h5 files each containing 16,278 images converted into numpy arrays. This allows for easy resuming but require 225 Go storage.

How to extract features

🎉 Check plismbench to perform the feature extraction of PLISM dataset and get run our robustness benchmark 🎉

In a nutshell, 91 folders will be created, each named by the slide_id and containing a features.npy file. This feature file is a numpy array of shape (16278, 3+d) where d is the output dimension of your model and 3 corresponds to (deepzoom_level, x_coordinate, y_coordinate). Tile coordinates are in the same order for each slide inside the dataset. No additional sorting is required to compare feature matrices between different slides (first element of each matrix corresponds to the same tile location).

225 Go are required to store WSI-level .h5 files, download approximately takes 10 minutes (32 workers). Then, ~10 Gb storage and 1h30 are necessary to extract all features with a ViT-B model, 16 CPUs and 1 Nvidia T4 (16Go).

License

This dataset is licensed under CC BY 4.0 licence.

Acknowledgments

We thank PLISM dataset's authors for their unique contribution.

Third-party licenses

PLISM dataset (Ochi et al., 2024) is distributed under CC BY 4.0 license.
Elastix (Klein et al., 2010; Shamonin et al., 2014) is distributed under Apache 2.0 license.

How to cite

If you are using this dataset, please cite the original article (Ochi et al., 2024) and our work as follows:

APA style

Filiot, A., Dop, N., Tchita, O., Riou, A., Peeters, T., Valter, D., Scalbert, M., Saillard, C., Robin, G., & Olivier, A. (2025). Distilling foundation models for robust and efficient models in digital pathology. arXiv. https://arxiv.org/abs/2501.16239

BibTex entry

@misc{filiot2025distillingfoundationmodelsrobust,
      title={Distilling foundation models for robust and efficient models in digital pathology}, 
      author={Alexandre Filiot and Nicolas Dop and Oussama Tchita and Auriane Riou and Thomas Peeters and Daria Valter and Marin Scalbert and Charlie Saillard and Geneviève Robin and Antoine Olivier},
      year={2025},
      eprint={2501.16239},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2501.16239}, 
}

References

(Ochi et al., 2024) Ochi, M., Komura, D., Onoyama, T. et al. Registered multi-device/staining histology image dataset for domain-agnostic machine learning models. Sci Data 11, 330 (2024).
(Klein et al., 2010) Klein, S., Staring, M., Murphy, K., Viergever, M. A., & Pluim, J. P. W. (2010). Elastix: A toolbox for intensity-based medical image registration. IEEE Transactions on Medical Imaging, 29(1), 196–205.