221 citations · 358 across the 11 of their papers we have counts for
11 papers
Evidence fusion with contextual discounting for multi-modality medical image segmentation
Ling Huang, Thierry Denoeux, Pierre Vera +1
As information sources are usually imperfect, it is necessary to take into account their reliability in multi-source information fusion tasks. In this paper, we propose a new deep…
A Quantitative Comparison between Shannon and Tsallis Havrda Charvat Entropies Applied to Cancer Outcome Prediction
Thibaud Brochet, Jérôme Lapuyade-Lahorgue, Pierre Vera +1
In this paper, we propose to quantitatively compare loss functions based on parameterized Tsallis-Havrda-Charvat entropy and classical Shannon entropy for the training of a deep ne…
Multi-Task Multi-Scale Learning For Outcome Prediction in 3D PET Images
Amine Amyar, Romain Modzelewski, Pierre Vera +2
Background and Objectives: Predicting patient response to treatment and survival in oncology is a prominent way towards precision medicine. To that end, radiomics was proposed as a…
Feature-enhanced Generation and Multi-modality Fusion based Deep Neural Network for Brain Tumor Segmentation with Missing MR Modalities
Tongxue Zhou, Stéphane Canu, Pierre Vera +1
Using multimodal Magnetic Resonance Imaging (MRI) is necessary for accurate brain tumor segmentation. The main problem is that not all types of MRIs are always available in clinica…
A Tri-attention Fusion Guided Multi-modal Segmentation Network
Tongxue Zhou, Su Ruan, Pierre Vera +1
In the field of multimodal segmentation, the correlation between different modalities can be considered for improving the segmentation results. Considering the correlation between…
Conditional generator and multi-sourcecorrelation guided brain tumor segmentation with missing MR modalities
Tongxue Zhou, Stéphane Canu, Pierre Vera +1
Brain tumor is one of the most high-risk cancers which causes the 5-year survival rate of only about 36%. Accurate diagnosis of brain tumor is critical for the treatment planning.…
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…