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.
Why Panoptic Quality should be avoided as a metric for assessing cell nuclei segmentation and classification in digital pathology
Preprint, 2022 (currently under review)
A. Foucart, O. Debeir, C. Decaestecker.
Evaluating participating methods in image analysis challenges: lessons from MoNuSAC 2020
Preprint, 2022 (currently under review)
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.
Preprint on ResearchSquare, 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