Noémie Moreau

According to our database, Noémie Moreau authored at least 8 papers between 2019 and 2022.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

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

PhD thesis, 2022

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

Proceedings of the 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society, 2022

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, Diana Mateus

CoRR, 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

Proceedings of the Medical Imaging 2021: Image Processing, Online, February 15-19, 2021, 2021

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

Proceedings of the Medical Imaging 2021: Computer-Aided Diagnosis, 2021

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

Proceedings of the 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society, 2020

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

Proceedings of the 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society, 2020

2019

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