221 citations · 221 across the 2 of their papers we have counts for
5 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…
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…
RADIOGAN: Deep Convolutional Conditional Generative adversarial Network To Generate PET Images
Amine Amyar, Su Ruan, Pierre Vera +2
One of the most challenges in medical imaging is the lack of data. It is proven that classical data augmentation methods are useful but still limited due to the huge variation in i…
Weakly Supervised PET Tumor Detection Using Class Response
Amine Amyar, Romain Modzelewski, Pierre Vera +2
One of the most challenges in medical imaging is the lack of data and annotated data. It is proven that classical segmentation methods such as U-NET are useful but still limited du…
Brain tumor segmentation with missing modalities via latent multi-source correlation representation
Tongxue Zhou, Stéphane Canu, Pierre Vera +1
Multimodal MR images can provide complementary information for accurate brain tumor segmentation. However, it's common to have missing imaging modalities in clinical practice. Sinc…