Kidney tumor segmentation using an ensembling multi-stage deep learning approach. A contribution to the KiTS19 challenge. - Gianmarco Santini; Noémie Moreau; Mathieu Rubeaux (CoRR, 2019)

Combining Superpixels and Deep Learning Approaches to Segment Active Organs in Metastatic Breast Cancer PET Images*. - Constance Fourcade; Ludovic Ferrer; Gianmarco Santini; Noémie Moreau; Caroline Rousseau; Marie Lacombe; Camille Guillerminet; Mathilde Colombié; Mario Campone; Diana Mateus; Mathieu Rubeaux (EMBC, 2020)

Deep learning approaches for bone and bone lesion segmentation on 18FDG PET/CT imaging in the context of metastatic breast cancer*. - Noémie Moreau; Caroline Rousseau; Constance Fourcade; Gianmarco Santini; Ludovic Ferrer; Marie Lacombe; Camille Guillerminet; Mario Campone; Mathilde Colombié; Mathieu Rubeaux; Nicolas Normand (EMBC, 2020)

Comparison between threshold-based and deep learning-based bone segmentation on whole-body CT images. - Noémie Moreau; Caroline Rousseau; Constance Fourcade; Gianmarco Santini; Ludovic Ferrer; Marie Lacombe; Camille Guillerminet; Pascal Jézéquel; Mario Campone; Nicolas Normand; Mathieu Rubeaux (Computer-Aided Diagnosis, 2021)

Automatic classification of benign and malignant kidney masses using radiomics. A retrospective study exploiting the KiTS19 dataset. - Gianmarco Santini; Yvon Nzoughet Obame; Constance Fourcade; Noémie Moreau; Mathieu Rubeaux (Image Processing, 2021)

Deformable Image Registration with Deep Network Priors: a Study on Longitudinal PET Images. - Constance Fourcade; Ludovic Ferrer; Noémie Moreau; Gianmarco Santini; Aishlinn Brennan; Caroline Rousseau; Marie Lacombe; Vincent Fleury; Mathilde Colombié; Pascal Jézéquel; Mario Campone; Mathieu Rubeaux; et al. (CoRR, 2021)

Influence of inputs for bone lesion segmentation in longitudinal 18F-FDG PET/CT imaging studies. - Noémie Moreau; Caroline Rousseau; Constance Fourcade; Gianmarco Santini; Ludovic Ferrer; Marie Lacombe; Camille Guillerminet; Mathilde Colombié; Pascal Jézéquel; Mario Campone; Mathieu Rubeaux; Nicolas Normand (EMBC, 2022)

Utilisation de l'apprentissage profond pour la segmentation et la caractérisation des images TEP/TDM FDG dans le cadre du cancer du sein métastatique. (Deep learning methods to segment and characterize PET/CT images in the context of metastatic breast cancer). - Noémie Moreau (2022)

GLOMNET: A Hover Deep Learning Model for Glomerulus Instance Segmentation. - Noémie Moreau; Michelle Shabani; Christoph Schell; Katarzyna Bozek (ISBI, 2024)

KPIs 2024 Challenge: Advancing Glomerular Segmentation from Patch- to Slide-Level. - Ruining Deng; Tianyuan Yao; Yucheng Tang; Junlin Guo; Siqi Lu; Juming Xiong; Lining Yu; Quan Huu Cap; Pengzhou Cai; Libin Lan; Ze Zhao; Adrian Galdran; et al. (CoRR, 2025)