70 citations · 73 across the 6 of their papers we have counts for
7 papers
AutoPETIII: The Tracer Frontier. What Frontier?
Zacharia Mesbah, Léo Mottay, Romain Modzelewski +4
For the last three years, the AutoPET competition gathered the medical imaging community around a hot topic: lesion segmentation on Positron Emitting Tomography (PET) scans. Each y…
Deep evidential fusion with uncertainty quantification and contextual discounting for multimodal medical image segmentation
Ling Huang, Su Ruan, Pierre Decazes +1
Single-modality medical images generally do not contain enough information to reach an accurate and reliable diagnosis. For this reason, physicians generally diagnose diseases base…
Lymphoma segmentation from 3D PET-CT images using a deep evidential network
Ling Huang, Su Ruan, Pierre Decazes +1
An automatic evidential segmentation method based on Dempster-Shafer theory and deep learning is proposed to segment lymphomas from three-dimensional Positron Emission Tomography (…
AI-Based Detection, Classification and Prediction/Prognosis in Medical Imaging: Towards Radiophenomics
Fereshteh Yousefirizi, Pierre Decazes, Amine Amyar +3
Artificial intelligence (AI) techniques have significant potential to enable effective, robust and automated image phenotyping including identification of subtle patterns. AI-based…
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…
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…
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…