no code implementations • 11 Apr 2023 • Tongxue Zhou, Alexandra Noeuveglise, Romain Modzelewski, Fethi Ghazouani, Sébastien Thureau, Maxime Fontanilles, Su Ruan
In this paper, we present a deep learning-based brain tumor recurrence location prediction network.
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 • 22 Apr 2020 • Tongxue Zhou, Su Ruan, Stéphane Canu
Due to their self-learning and generalization ability over large amounts of data, deep learning recently has also gained great interest in multi-modal medical image segmentation.
no code implementations • 14 Apr 2020 • Tongxue Zhou, Stéphane Canu, Su Ruan
The coronavirus disease (COVID-19) pandemic has led to a devastating effect on the global public health.
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.