Extended scale invariant local binary pattern for background subtraction. - Dongdong Zeng; Ming Zhu 013; Fang Xu; Tongxue Zhou (IET Image Process., 2018)

Deep Learning Model Integrating Dilated Convolution and Deep Supervision for Brain Tumor Segmentation in Multi-parametric MRI. - Tongxue Zhou; Su Ruan; Haigen Hu; Stéphane Canu (MLMI@MICCAI, 2019)

A review: Deep learning for medical image segmentation using multi-modality fusion. - Tongxue Zhou; Su Ruan; Stéphane Canu (Array, 2019)

3D Medical Multi-modal Segmentation Network Guided by Multi-source Correlation Constraint. - Tongxue Zhou; Stéphane Canu; Pierre Vera; Su Ruan (ICPR, 2020)

Lymphoma Segmentation in PET Images Based on Multi-view and Conv3D Fusion Strategy. - Haigen Hu; Leizhao Shen; Tongxue Zhou; Pierre Decazes; Pierre Vera; Su Ruan (ISBI, 2020)

A Multi-Modality Fusion Network Based on Attention Mechanism for Brain Tumor Segmentation. - Tongxue Zhou; Su Ruan; Yu Guo; Stéphane Canu (ISBI, 2020)

Brain Tumor Segmentation with Missing Modalities via Latent Multi-source Correlation Representation. - Tongxue Zhou; Stéphane Canu; Pierre Vera; Su Ruan (MICCAI (4), 2020)

Fusion based on attention mechanism and context constraint for multi-modal brain tumor segmentation. - Tongxue Zhou; Stéphane Canu; Su Ruan (Comput. Medical Imaging Graph., 2020)

An automatic COVID-19 CT segmentation network using spatial and channel attention mechanism. - Tongxue Zhou; Stéphane Canu; Su Ruan (CoRR, 2020)

A Dual Supervision Guided Attentional Network for Multimodal MR Brain Tumor Segmentation. - Tongxue Zhou; Stéphane Canu; Pierre Vera; Su Ruan (MICAD, 2021)

Feature-enhanced generation and multi-modality fusion based deep neural network for brain tumor segmentation with missing MR modalities. - Tongxue Zhou; Stéphane Canu; Pierre Vera; Su Ruan (Neurocomputing, 2021)

Automatic COVID-19 CT segmentation using U-Net integrated spatial and channel attention mechanism. - Tongxue Zhou; Stéphane Canu; Su Ruan (Int. J. Imaging Syst. Technol., 2021)

Latent Correlation Representation Learning for Brain Tumor Segmentation With Missing MRI Modalities. - Tongxue Zhou; Stéphane Canu; Pierre Vera; Su Ruan (IEEE Trans. Image Process., 2021)

Prediction of Brain Tumor Recurrence Location Based on Kullback-Leibler Divergence and Nonlinear Correlation Learning. - Tongxue Zhou; Alexandra Noeuveglise; Fethi Ghazouani; Romain Modzelewski; Sébastien Thureau; Maxime Fontanilles; Su Ruan (ICPR, 2022)

A Tri-Attention fusion guided multi-modal segmentation network. - Tongxue Zhou; Su Ruan; Pierre Vera; Stéphane Canu (Pattern Recognit., 2022)

Missing Data Imputation via Conditional Generator and Correlation Learning for Multimodal Brain Tumor Segmentation. - Tongxue Zhou; Pierre Vera; Stéphane Canu; Su Ruan (Pattern Recognit. Lett., 2022)

Deep learning for semantic segmentation in multimodal medical images : application on brain tumor segmentation from multimodal magnetic resonance imaging. (Apprentissage profond pour la segmentation sémantique d'images médicales multimodales : applications à la segmentation des tumeurs cérébrales à partir d'images IRM multimodales). - Tongxue Zhou (2022)

Modality-level cross-connection and attentional feature fusion based deep neural network for multi-modal brain tumor segmentation. - Tongxue Zhou (Biomed. Signal Process. Control., 2023)

A literature survey of MR-based brain tumor segmentation with missing modalities. - Tongxue Zhou; Su Ruan; Haigen Hu (Comput. Medical Imaging Graph., 2023)

Prediction of brain tumor recurrence location based on multi-modal fusion and nonlinear correlation learning. - Tongxue Zhou; Alexandra Noeuveglise; Romain Modzelewski; Fethi Ghazouani; Sébastien Thureau; Maxime Fontanilles; Su Ruan (Comput. Medical Imaging Graph., 2023)

Uncertainty quantification and attention-aware fusion guided multi-modal MR brain tumor segmentation. - Tongxue Zhou; Shan Zhu (Comput. Biol. Medicine, 2023)

Feature fusion and latent feature learning guided brain tumor segmentation and missing modality recovery network. - Tongxue Zhou (Pattern Recognit., 2023)

Multi-modal brain tumor segmentation via disentangled representation learning and region-aware contrastive learning. - Tongxue Zhou (Pattern Recognit., 2024)

M2GCNet: Multi-Modal Graph Convolution Network for Precise Brain Tumor Segmentation Across Multiple MRI Sequences. - Tongxue Zhou (IEEE Trans. Image Process., 2024)

Learning deep feature representations for multi-modal MR brain tumor segmentation. - Tongxue Zhou; Zheng Wang; Xiaohui Liu 001; Weibo Liu 001; Shan Zhu (Neurocomputing, 2025)

Boundary-aware and cross-modal fusion network for enhanced multi-modal brain tumor segmentation. - Tongxue Zhou (Pattern Recognit., 2025)