29 citations · 42 across the 6 of their papers we have counts for
6 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…
Fed-BioMed: Open, Transparent and Trusted Federated Learning for Real-world Healthcare Applications
Francesco Cremonesi, Marc Vesin, Sergen Cansiz +16
The real-world implementation of federated learning is complex and requires research and development actions at the crossroad between different domains ranging from data science, t…
Prediction of brain tumor recurrence location based on multi-modal fusion and nonlinear correlation learning
Tongxue Zhou, Alexandra Noeuveglise, Romain Modzelewski +4
Brain tumor is one of the leading causes of cancer death. The high-grade brain tumors are easier to recurrent even after standard treatment. Therefore, developing a method to predi…
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…
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…