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README.md
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## Contact
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For any additional questions or comments, contact Alexandre Filiot (`alexandre.filiot@
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## Acknowledgements
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We thank [DINOv2](https://github.com/facebookresearch/dinov2) authors for the amazing contribution [1].
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###
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This work was granted access to the HPC resources of IDRIS under the allocation 2023-A0141012519 made by GENCI.
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###
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The results published here are partly based upon data generated by the TCGA Research Network: [https://www.cancer.gov/tcga](https://www.cancer.gov/tcga).
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The Genotype-Tissue Expression (GTEx) Project was supported by the Common Fund of the Office of the Director of the National Institutes of Health, and by NCI, NHGRI, NHLBI, NIDA, NIMH, and NINDS. The data used for the analyses described in this manuscript were obtained from the GTEx Portal on 07/01/2023.
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## References
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Here is the uniformized bibliography in APA style:
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1. Oquab, M., Darcet, T., Moutakanni, T., Vo, H., Szafraniec, M., Khalidov, V., Fernandez, P., Haziza, D., Massa, F., El-Nouby, A., Assran, M., Ballas, N., Galuba, W., Howes, R., Huang, P.-Y., Li, S.-W., Misra, I., Rabbat, M., Sharma, V., Synnaeve, G., Xu, H., Jegou, H., Mairal, J., Labatut, P., Joulin, A., & Bojanowski, P. (2024). Dinov2: Learning robust visual features without supervision. *arXiv*.
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2. Clark, K., Vendt, B., Smith, K., Freymann, J., Kirby, J., Koppel, P., Moore, S., Phillips, S., Maffitt, D., Pringle, M., Tarbox, L., & Prior, F. (2013). The Cancer Imaging Archive (TCIA): Maintaining and operating a public information repository. *Journal of Digital Imaging, 26*(6), 1045–1057. Springer Science and Business Media LLC. https://doi.org/10.1007/s10278-013-9622-7
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## Contact
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For any additional questions or comments, contact Alexandre Filiot (`alexandre.filiot@owkin.com`).
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## Acknowledgements
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We thank [DINOv2](https://github.com/facebookresearch/dinov2) authors for the amazing contribution [1].
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### _Computing resources_
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This work was granted access to the HPC resources of IDRIS under the allocation 2023-A0141012519 made by GENCI.
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### _Datasets_
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The results published here are partly based upon data generated by the TCGA Research Network: [https://www.cancer.gov/tcga](https://www.cancer.gov/tcga).
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The Genotype-Tissue Expression (GTEx) Project was supported by the Common Fund of the Office of the Director of the National Institutes of Health, and by NCI, NHGRI, NHLBI, NIDA, NIMH, and NINDS. The data used for the analyses described in this manuscript were obtained from the GTEx Portal on 07/01/2023.
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## References
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1. Oquab, M., Darcet, T., Moutakanni, T., Vo, H., Szafraniec, M., Khalidov, V., Fernandez, P., Haziza, D., Massa, F., El-Nouby, A., Assran, M., Ballas, N., Galuba, W., Howes, R., Huang, P.-Y., Li, S.-W., Misra, I., Rabbat, M., Sharma, V., Synnaeve, G., Xu, H., Jegou, H., Mairal, J., Labatut, P., Joulin, A., & Bojanowski, P. (2024). Dinov2: Learning robust visual features without supervision. *arXiv*.
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2. Clark, K., Vendt, B., Smith, K., Freymann, J., Kirby, J., Koppel, P., Moore, S., Phillips, S., Maffitt, D., Pringle, M., Tarbox, L., & Prior, F. (2013). The Cancer Imaging Archive (TCIA): Maintaining and operating a public information repository. *Journal of Digital Imaging, 26*(6), 1045–1057. Springer Science and Business Media LLC. https://doi.org/10.1007/s10278-013-9622-7
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