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

no code implementations • 1 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

AI-Based Detection, Classification and Prediction/Prognosis in Medical Imaging: Towards Radiophenomics

no code implementations • 20 Oct 2021 • Fereshteh Yousefirizi, Pierre Decazes, Amine Amyar, Su Ruan, Babak Saboury, Arman Rahmim

Artificial intelligence (AI) techniques have significant potential to enable effective, robust and automated image phenotyping including identification of subtle patterns.

Translation

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

no code implementations • 19 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 implementations • 18 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