Search Results for author: Romain Modzelewski

Found 6 papers, 1 papers with code

AutoPETIII: The Tracer Frontier. What Frontier?

no code implementations19 Sep 2024 Zacharia Mesbah, Léo Mottay, Romain Modzelewski, Pierre Decazes, Sébastien Hapdey, Su Ruan, Sébastien Thureau

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.

Lesion Segmentation Segmentation

Fed-BioMed: Open, Transparent and Trusted Federated Learning for Real-world Healthcare Applications

1 code implementation24 Apr 2023 Francesco Cremonesi, Marc Vesin, Sergen Cansiz, Yannick Bouillard, Irene Balelli, Lucia Innocenti, Santiago Silva, Samy-Safwan Ayed, Riccardo Taiello, Laetita Kameni, Richard Vidal, Fanny Orlhac, Christophe Nioche, Nathan Lapel, Bastien Houis, Romain Modzelewski, Olivier Humbert, Melek Önen, Marco Lorenzi

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, to software programming, networking, and security.

Federated Learning

Multi-Task Multi-Scale Learning For Outcome Prediction in 3D PET Images

no code implementations1 Mar 2022 Amine Amyar, Romain Modzelewski, Pierre Vera, Vincent Morard, Su Ruan

Conclusions: We show that, by using a multi-task learning approach, we can boost the performance of radiomic analysis by extracting rich information of intratumoral and peritumoral regions.

Inductive Bias Multi-Task Learning

RADIOGAN: Deep Convolutional Conditional Generative adversarial Network To Generate PET Images

no code implementations19 Mar 2020 Amine Amyar, Su Ruan, Pierre Vera, Pierre Decazes, Romain Modzelewski

Using generative adversarial networks (GAN) is a promising way to address this problem, however, it is challenging to train one model to generate different classes of lesions.

Data Augmentation Generative Adversarial Network

Weakly Supervised PET Tumor Detection Using Class Response

no code implementations18 Mar 2020 Amine Amyar, Romain Modzelewski, Pierre Vera, Vincent Morard, Su Ruan

In this paper, we present a novel approach to locate different type of lesions in positron emission tomography (PET) images using only a class label at the image-level.

Weakly-supervised Learning

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