Publications

List of publications
A. Foucart, O. Debeir, C. Decaestecker.
Shortcomings and areas for improvement in digital pathology image segmentation challenges
Computerized Medical Imaging and Graphics (103), 2023
doi:10.1016/j.compmedimag.2022.102155 Download
A. Foucart, O. Debeir, C. Decaestecker.
Why Panoptic Quality should be avoided as a metric for assessing cell nuclei segmentation and classification in digital pathology
Preprint, 2022 (currently under review)
doi:10.21203/rs.3.rs-2272573/v1 Download
A. Foucart, O. Debeir, C. Decaestecker.
Evaluating participating methods in image analysis challenges: lessons from MoNuSAC 2020
Preprint, 2022 (currently under review)
doi:10.13140/RG.2.2.11627.00801 Download
A. Foucart, O. Debeir, C. Decaestecker.
Comments on "Monusac2020: A Multi-Organ Nuclei Segmentation and Classification Challenge"
IEEE Trans. Medical Imaging, 2022
doi:10.1109/TMI.2022.3156023 Download
A. Foucart, O. Debeir, C. Decaestecker.
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, 2021
doi:10.1117/12.2604307 Download
A. Foucart, O. Debeir, C. Decaestecker.
SNOW Supervision in Digital Pathology: Managing Imperfect Annotations for Segmentation in Deep Learning.
Preprint on ResearchSquare, 2020
doi:10.21203/rs.3.rs-116512/v1 Download
Y-R. Van Eycke, A. Foucart, C. Decaestecker
Strategies to Reduce the Expert Supervision Required for Deep Learning-Based Segmentation of Histopathological Images.
Frontiers in Medicine (6), 2019
doi:10.3389/fmed.2019.00222
A. Foucart, O. Debeir, C. Decaestecker.
SNOW: Semi-Supervised, NOisy and/or Weak Data for Deep Learning in Digital Pathology.
ISBI, 2019
doi:10.1109/ISBI.2019.8759545 Download
A. Foucart, O. Debeir, C. Decaestecker.
Artifact Identification in Digital Pathology from Weak and Noisy Supervision with Deep Residual Networks.
Cloud'Tech, 2018
doi:10.1109/CloudTech.2018.8713350 Download