Artifact Identification in Digital Pathology from Weak and Noisy Supervision with Deep Residual Networks. - Adrien Foucart; Olivier Debeir; Christine Decaestecker (Cloudtech, 2018)

SNOW: Semi-Supervised, Noisy And/Or Weak Data For Deep Learning In Digital Pathology. - Adrien Foucart; Olivier Debeir; Christine Decaestecker (ISBI, 2019)

Comments on "MoNuSAC2020: A Multi-Organ Nuclei Segmentation and Classification Challenge". - Adrien Foucart; Olivier Debeir; Christine Decaestecker (IEEE Trans. Medical Imaging, 2022)

Assessing Local Descriptors for Feature-Based Registration of Whole-Slide Images. - Arthur Elskens; Adrien Foucart; Egor Zindy; Olivier Debeir; Christine Decaestecker (SIPAIM, 2023)

Finding the best channel for tissue segmentation in whole-slide images. - Adrien Foucart; Arthur Elskens; Olivier Debeir; Christine Decaestecker (SIPAIM, 2023)

Shortcomings and areas for improvement in digital pathology image segmentation challenges. - Adrien Foucart; Olivier Debeir; Christine Decaestecker (Comput. Medical Imaging Graph., 2023)

Evaluating participating methods in image analysis challenges: Lessons from MoNuSAC 2020. - Adrien Foucart; Olivier Debeir; Christine Decaestecker (Pattern Recognit., 2023)