Publications

List of publications

For the definitive versions of each journal/conference publication, refer to the DOI link.

A. Foucart
Comprendre et évaluer les intelligences artificielles
Séminaire donné au Skeptics in the Pub Liège (décembre 2023)
A. Foucart
Intelligence artificielle: une nouvelle ère?
Séminaire donné en faculté de Droit (mars 2023)
A. Foucart, A. Elskens, O. Debeir, C. Decaestecker
Finding the best channel for tissue segmentation in whole-slide images
Proc. 19th International Symposium on Medical Information Processing and Analysis (November 2023)
A. Elskens, A. Foucart, E. Zindy, O. Debeir, C. Decaestecker
Assessing Local Descriptors for Feature-Based Registration of Whole-Slide Images
Proc. 19th International Symposium on Medical Information Processing and Analysis (November 2023)
A. Foucart, O. Debeir, C. Decaestecker.
Panoptic Quality should be avoided as a metric for assessing cell nuclei segmentation and classification in digital pathology
Scientific Reports 13 (8614), 2023
A. Foucart, O. Debeir, C. Decaestecker.
Evaluating participating methods in image analysis challenges: lessons from MoNuSAC 2020
Pattern Recognition (141), 2023
A. Foucart, O. Debeir, C. Decaestecker.
Shortcomings and areas for improvement in digital pathology image segmentation challenges
Computerized Medical Imaging and Graphics (103), 2023
A. Foucart, O. Debeir, C. Decaestecker.
Comments on "Monusac2020: A Multi-Organ Nuclei Segmentation and Classification Challenge"
IEEE Trans. Medical Imaging, 2022
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
A. Foucart, O. Debeir, C. Decaestecker.
SNOW Supervision in Digital Pathology: Managing Imperfect Annotations for Segmentation in Deep Learning.
Report, 2020
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
A. Foucart, O. Debeir, C. Decaestecker.
SNOW: Semi-Supervised, NOisy and/or Weak Data for Deep Learning in Digital Pathology.
ISBI, 2019
A. Foucart, O. Debeir, C. Decaestecker.
Artifact Identification in Digital Pathology from Weak and Noisy Supervision with Deep Residual Networks.
Cloud'Tech, 2018