Communications publiées lors de congrès ou colloques nationaux et internationaux (39)
  1. 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.
  2. 3. 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
  3. 4. 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
  4. 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
  5. 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
  6. 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
  7. 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
  8. 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
  9. 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
  10. 11. Van Eycke, Y.-R., Debeir, O., Verset, L., Demetter, P., Salmon, I., & Decaestecker, C. (2015). High-Throughput Analysis of Tissue-Based Biomarkers in Digital Pathology. In EMBC'15 Proceedings (IEEE Engineering in Medicine and Biology Society) (pp. 7732 - 7735) IEEE. doi:10.1109/EMBC.2015.7320184
  11. 12. Van Eycke, Y.-R., Debeir, O., Verset, L., Demetter, P., Salmon, I., & Decaestecker, C. (2015). Automated Tissue Microarray Image Processing in Digital Pathology. In Proceedings of the Fifth joint WIC/IEEE SP Symposium on Information Theory and Signal Processing in the Benelux. ((8 p.)). IEEE.
  12. 13. Moles Lopez, X., Debeir, O., Maris, C., Roland, I., Salmon, I., & Decaestecker, C. (2010). Ki-67 hot-spots detection on glioblastoma tissue sections. In Proc. of the 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro (ISBI 2010) (pp. pp 149-152) IEEE 2010.
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