Articles dans des revues avec comité de lecture (3)
  1. 1. 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
  2. 2. 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
  3. 3. 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
    4.   Communications publiées lors de congrès ou colloques nationaux et internationaux (4)
  4. 1. 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
  5. 2. 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
  6. 3. 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
  7. 4. 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)
    8.   Rapports de recherche, comptes rendus, lettres à l'éditeur, working papers (1)
  8. 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
    9.   Thèses et mémoires (1)
  9. 1. Foucart, A. (2022). Impact of real-world annotations on the training and evaluation of deep learning algorithms in digital pathology (Thèse doctorale non-publiée). Université libre de Bruxelles, Ecole polytechnique de Bruxelles – Biomédical, Bruxelles.