Tongxue Zhou
Orcid: 0000-0003-3110-4884
According to our database1,
Tongxue Zhou
authored at least 24 papers
between 2018 and 2024.
Collaborative distances:
Collaborative distances:
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Bibliography
2024
M2GCNet: Multi-Modal Graph Convolution Network for Precise Brain Tumor Segmentation Across Multiple MRI Sequences.
IEEE Trans. Image Process., 2024
Multi-modal brain tumor segmentation via disentangled representation learning and region-aware contrastive learning.
Pattern Recognit., 2024
2023
Feature fusion and latent feature learning guided brain tumor segmentation and missing modality recovery network.
Pattern Recognit., September, 2023
Uncertainty quantification and attention-aware fusion guided multi-modal MR brain tumor segmentation.
Comput. Biol. Medicine, September, 2023
Prediction of brain tumor recurrence location based on multi-modal fusion and nonlinear correlation learning.
Comput. Medical Imaging Graph., June, 2023
Comput. Medical Imaging Graph., March, 2023
Modality-level cross-connection and attentional feature fusion based deep neural network for multi-modal brain tumor segmentation.
Biomed. Signal Process. Control., March, 2023
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).
PhD thesis, 2022
Missing Data Imputation via Conditional Generator and Correlation Learning for Multimodal Brain Tumor Segmentation.
Pattern Recognit. Lett., 2022
Pattern Recognit., 2022
Prediction of Brain Tumor Recurrence Location Based on Kullback-Leibler Divergence and Nonlinear Correlation Learning.
Proceedings of the 26th International Conference on Pattern Recognition, 2022
2021
Latent Correlation Representation Learning for Brain Tumor Segmentation With Missing MRI Modalities.
IEEE Trans. Image Process., 2021
Automatic COVID-19 CT segmentation using U-Net integrated spatial and channel attention mechanism.
Int. J. Imaging Syst. Technol., 2021
Feature-enhanced generation and multi-modality fusion based deep neural network for brain tumor segmentation with missing MR modalities.
Neurocomputing, 2021
A Dual Supervision Guided Attentional Network for Multimodal MR Brain Tumor Segmentation.
Proceedings of 2021 International Conference on Medical Imaging and Computer-Aided Diagnosis, 2021
2020
An automatic COVID-19 CT segmentation network using spatial and channel attention mechanism.
CoRR, 2020
Fusion based on attention mechanism and context constraint for multi-modal brain tumor segmentation.
Comput. Medical Imaging Graph., 2020
Brain Tumor Segmentation with Missing Modalities via Latent Multi-source Correlation Representation.
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2020, 2020
A Multi-Modality Fusion Network Based on Attention Mechanism for Brain Tumor Segmentation.
Proceedings of the 17th IEEE International Symposium on Biomedical Imaging, 2020
Proceedings of the 17th IEEE International Symposium on Biomedical Imaging, 2020
3D Medical Multi-modal Segmentation Network Guided by Multi-source Correlation Constraint.
Proceedings of the 25th International Conference on Pattern Recognition, 2020
2019
Array, 2019
Deep Learning Model Integrating Dilated Convolution and Deep Supervision for Brain Tumor Segmentation in Multi-parametric MRI.
Proceedings of the Machine Learning in Medical Imaging - 10th International Workshop, 2019
2018
IET Image Process., 2018