221 citations · 260 across the 10 of their papers we have counts for
17 papers
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…
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…
Deep PET/CT fusion with Dempster-Shafer theory for lymphoma segmentation
Ling Huang, Thierry Denoeux, David Tonnelet +2
Lymphoma detection and segmentation from whole-body Positron Emission Tomography/Computed Tomography (PET/CT) volumes are crucial for surgical indication and radiotherapy. Designin…
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.…
Evidential segmentation of 3D PET/CT images
Ling Huang, Su Ruan, Pierre Decazes +1
PET and CT are two modalities widely used in medical image analysis. Accurately detecting and segmenting lymphomas from these two imaging modalities are critical tasks for cancer s…