no code implementations • 17 Nov 2023 • Aghiles Kebaili, Jérôme Lapuyade-Lahorgue, Pierre Vera, Su Ruan
We conduct experiments on two publicly available datasets, MICCAI's Brain Tumor Segmentation Challenge (BRATS), and Head and Neck Tumor Segmentation Challenge (HECKTOR), demonstrating the effectiveness of our method on different medical imaging modalities.
1 code implementation • 23 Jun 2022 • Ling Huang, Thierry Denoeux, Pierre Vera, Su Ruan
As information sources are usually imperfect, it is necessary to take into account their reliability in multi-source information fusion tasks.
no code implementations • 22 Mar 2022 • Thibaud Brochet, Jérôme Lapuyade-Lahorgue, Pierre Vera, Su Ruan
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 network in the case of small datasets which are usually encountered in medical applications.
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.
no code implementations • 8 Nov 2021 • Tongxue Zhou, Stéphane Canu, Pierre Vera, Su Ruan
Based on the fact that there is a strong correlation between MR modalities of the same patient, in this work, we propose a novel brain tumor segmentation network in the case of missing one or more modalities.
no code implementations • 2 Nov 2021 • Tongxue Zhou, Su Ruan, Pierre Vera, Stéphane Canu
Considering the correlation between different MR modalities, in this paper, we propose a multi-modality segmentation network guided by a novel tri-attention fusion.
no code implementations • 27 May 2021 • Tongxue Zhou, Stéphane Canu, Pierre Vera, Su Ruan
The proposed network consists of a conditional generator, a correlation constraint network and a segmentation network.
no code implementations • 13 Apr 2021 • Tongxue Zhou, Stéphane Canu, Pierre Vera, Su Ruan
In addition, multi-modal MR images can provide complementary information for accurate brain tumor segmentation.
no code implementations • 5 Feb 2021 • Tongxue Zhou, Stéphane Canu, Pierre Vera, Su Ruan
Our network includes N model-independent encoding paths with N image sources, a correlation constraint block, a feature fusion block, and a decoding path.
no code implementations • 19 Mar 2020 • Tongxue Zhou, Stéphane Canu, Pierre Vera, Su Ruan
Multimodal MR images can provide complementary information for accurate brain tumor segmentation.
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.
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.