Articles dans des revues sans comité de lecture (4)
  1. 3. Debeir, O., Allard, J., Decaestecker, C., & Hermand, J.-P. (2019). Characterization of Posidonia Oceanica Seagrass Aerenchyma through Whole Slide Imaging: A Pilot Study. arXiv.org.
  2. 4. Decaestecker, C., De Clercq, S., & Salmon, I. (2018). S100A4, a key factor in glioblastoma biology. Translational cancer research, 7(S1), S71-S73. doi:10.21037/tcr.2017.12.27
    3.   Communications publiées lors de congrès ou colloques nationaux et internationaux (37)
  3. 1. Foucart, A., Elskens, A., & Decaestecker, C. (2025). Ranking the scores of algorithms with confidence. ESANN 2025 European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning(23-25 April 2025: Bruges, Belgium)
  4. 2. Galvez Jiménez, L., Franzin, A., & Decaestecker, C. (2024). Training Data Selection to Improve Multi-class Instance Segmentation in Digital Pathology. In Proceedings of the 2023 10th International Conference on Bioinformatics Research and Applications (p. 27–33) ACM.
  5. 3. Foucart, A., Elskens, A., Debeir, O., & Decaestecker, C. (2024). Finding the best channel for tissue segmentation in whole-slide images. Proceedings of the 19th International Symposium on Medical Information Processing and Analysis SIPAIM(15-17/11/2023: Mexico City, Mexico) doi:10.1109/SIPAIM56729.2023.10373416
  6. 4. Elskens, A., Foucart, A., Zindy, E., Debeir, O., & Decaestecker, C. (2024). Assessing Local Descriptors for Feature-Based Registration of Whole-Slide Images. Proceedings of the 19th International Symposium on Medical Information Processing and Analysis, SIPAIM(15-17/11/2023: Mexico City, Mexico) doi:10.1109/SIPAIM56729.2023.10373514
  7. 5. Lavis, P., Pingitore, J., Doumont, G., Gabaret, A., Van Simaeys, G., Lacroix, S., Passon, N., Van Heymbeek, C., De Maeseneire, C., Huaux, F., Decaestecker, C., Salmon, I., Cardozo, A. K., Goldman, S., & Bondue, B. (2023). Fibroblast Activation Protein Inhibitor, a Promising Radiotracer inFibrogenesis. American journal of respiratory and critical care medicine,(207), A4704. doi:https://doi.org/10.1164/ajrccm-conference.2023.207.1_MeetingAbstracts.A4704
  8. 6. Foucart, A., Debeir, O., & Decaestecker, C. (2021). Processing multi-expert annotations in digital pathology: A study of the Gleason2019 challenge. Proc. SPIE 12088, 17th International Symposium on Medical Information Processing and Analysis. Vol. 120880X International Symposium on Medical Information Processing and Analysis(17th: Campinas, Brazil). doi:10.1117/12.2604307
  9. 7. Debeir, O., & Decaestecker, C. (2019). Data augmentation for training deep regression for in vitro cell detection. Fifth International Conference on Advances in Biomedical Engineering (ICABME) (pp. 1--3) International Conference on Advances in Biomedical Engineering (ICABME)(October 17-19, 2019: Lebanon). doi:10.1109/ICABME47164.2019.8940275
  10. 8. Foucart, A., Debeir, O., & Decaestecker, C. (2019). SNOW: Semi-Supervised, NOisy and/or Weak Data for Deep Learning in Digital Pathology. In 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019) (pp. 1869-1872) IEEE. doi:10.1109/ISBI.2019.8759545
  11. 9. Foucart, A., Debeir, O., & Decaestecker, C. (2018). Artifact Identification in Digital Pathology from Weak and Noisy Supervision with Deep Residual Networks. The 4th International Conference on Cloud Computing Technologies and Application (CloudTech'18)(Novembre 26-28, 2018: Brussels, Belgium) doi:10.1109/CloudTech.2018.8713350
  12. 10. Van Eycke, Y.-R., Allard, J., Derock, M., Salmon, I., Debeir, O., & Decaestecker, C. (2016). Image normalization for quantitative immunohistochemistry in digital pathology. In 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI) (pp. 795 - 798) IEEE. doi:10.1109/ISBI.2016.7493386
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