10 citations · 12 across the 4 of their papers we have counts for
6 papers
MICS : Multi-steps, Inverse Consistency and Symmetric deep learning registration network
Théo Estienne, Maria Vakalopoulou, Enzo Battistella +5
Deformable registration consists of finding the best dense correspondence between two different images. Many algorithms have been published, but the clinical application was made d…
Exploring Deep Registration Latent Spaces
Théo Estienne, Maria Vakalopoulou, Stergios Christodoulidis +8
Explainability of deep neural networks is one of the most challenging and interesting problems in the field. In this study, we investigate the topic focusing on the interpretabilit…
Weakly supervised pan-cancer segmentation tool
Marvin Lerousseau, Marion Classe, Enzo Battistella +8
The vast majority of semantic segmentation approaches rely on pixel-level annotations that are tedious and time consuming to obtain and suffer from significant inter and intra-expe…
Brain tumor segmentation with self-ensembled, deeply-supervised 3D U-net neural networks: a BraTS 2020 challenge solution
Theophraste Henry, Alexandre Carre, Marvin Lerousseau +4
Brain tumor segmentation is a critical task for patient's disease management. In order to automate and standardize this task, we trained multiple U-net like neural networks, mainly…
Deep learning based registration using spatial gradients and noisy segmentation labels
Théo Estienne, Maria Vakalopoulou, Enzo Battistella +6
Image registration is one of the most challenging problems in medical image analysis. In the recent years, deep learning based approaches became quite popular, providing fast and p…
Weakly supervised multiple instance learning histopathological tumor segmentation
Marvin Lerousseau, Maria Vakalopoulou, Marion Classe +7
Histopathological image segmentation is a challenging and important topic in medical imaging with tremendous potential impact in clinical practice. State of the art methods rely on…