Articles dans des revues avec comité de lecture (5)
  1. 1. Foucart, A., Debeir, O., & Decaestecker, C. (2023). Panoptic quality should be avoided as a metric for assessing cell nuclei segmentation and classification in digital pathology. Scientific reports, 13(1), 8614. doi:10.1038/s41598-023-35605-7
  2. 2. Foucart, A., Debeir, O., & Decaestecker, C. (2023). Evaluating participating methods in image analysis challenges: lessons from MoNuSAC 2020. Pattern recognition., 109600. doi:10.1016/j.patcog.2023.109600
  3. 3. Foucart, A., Debeir, O., & Decaestecker, C. (2022). Shortcomings and areas for improvement in digital pathology image segmentation challenges. Computerized medical imaging and graphics. doi:10.1016/j.compmedimag.2022.102155
  4. 4. Foucart, A., Debeir, O., & Decaestecker, C. (2022). Comments on “MoNuSAC2020: A Multi-Organ Nuclei Segmentation and Classification Challenge”. IEEE transactions on medical imaging, 41(4), 997-999. doi:10.1109/TMI.2022.3156023
  5. 5. Van Eycke, Y.-R., Foucart, A., & Decaestecker, C. (2019). Strategies to Reduce the Expert Supervision Required for Deep Learning-Based Segmentation of Histopathological Images. Frontiers in Medicine, 6, 222. doi:10.3389/fmed.2019.00222
    6.   Communications publiées lors de congrès ou colloques nationaux et internationaux (6)
  6. 1. 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
  7. 2. 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
  8. 3. 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. 4. 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
  10. 5. 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
  11. 6. Foucart, A., & Debeir, O. (2012). Unsupervised vehicle detection in traffic scene using distributed one class classifiers. International Symposium on signal, Image, Video and Communications (6 June 2012)
    12.   Rapports de recherche, comptes rendus, lettres à l'éditeur, working papers (1)
  12. 1. Foucart, A., Debeir, O., & Decaestecker, C. (2020). SNOW supervision in digital pathology: Managing imperfect annotations for segmentation in deep learning. doi:10.21203/rs.3.rs-116512/v1
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