221 citations · 248 across the 6 of their papers we have counts for
6 papers
Latent Correlation Representation Learning for Brain Tumor Segmentation with Missing MRI Modalities
Tongxue Zhou, Stéphane Canu, Pierre Vera +1
Magnetic Resonance Imaging (MRI) is a widely used imaging technique to assess brain tumor. Accurately segmenting brain tumor from MR images is the key to clinical diagnostics and t…
Deep learning using Havrda-Charvat entropy for classification of pulmonary endomicroscopy
Thibaud Brochet, Jerome Lapuyade-Lahorgue, Sebastien Bougleux +2
Pulmonary optical endomicroscopy (POE) is an imaging technology in real time. It allows to examine pulmonary alveoli at a microscopic level. Acquired in clinical settings, a POE im…
3D Medical Multi-modal Segmentation Network Guided by Multi-source Correlation Constraint
Tongxue Zhou, Stéphane Canu, Pierre Vera +1
In the field of multimodal segmentation, the correlation between different modalities can be considered for improving the segmentation results. In this paper, we propose a multi-mo…
Belief function-based semi-supervised learning for brain tumor segmentation
Ling Huang, Su Ruan, Thierry Denoeux
Precise segmentation of a lesion area is important for optimizing its treatment. Deep learning makes it possible to detect and segment a lesion field using annotated data. However,…
Covid-19 classification with deep neural network and belief functions
Ling Huang, Su Ruan, Thierry Denoeux
Computed tomography (CT) image provides useful information for radiologists to diagnose Covid-19. However, visual analysis of CT scans is time-consuming. Thus, it is necessary to d…
Medical Image Synthesis with Context-Aware Generative Adversarial Networks
Dong Nie, Roger Trullo, Caroline Petitjean +2
Computed tomography (CT) is critical for various clinical applications, e.g., radiotherapy treatment planning and also PET attenuation correction. However, CT exposes radiation dur…