Peer-reviewed journal articles (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.   Papers published in national and international conferences or symposium proceedings (6)
  6. 1. 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. 2. 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
  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.   Research reports, book reviews, letters to the editor, 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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