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Xiangde Luo
Xiangde Luo
Stanford University/West China Hospital/UESTC
Verified email at stanford.edu - Homepage
Title
Cited by
Cited by
Year
Transunet: Transformers make strong encoders for medical image segmentation
J Chen, Y Lu, Q Yu, X Luo, E Adeli, Y Wang, L Lu, AL Yuille, Y Zhou
International Conference on Machine Learning Workshop, 2021
48642021
Semi-supervised medical image segmentation through dual-task consistency
X Luo, J Chen, T Song, Y Chen, G Wang, S Zhang
AAAI Conference on Artificial Intelligence 35 (10), 8801-8809, 2021
5322021
Efficient semi-supervised gross target volume of nasopharyngeal carcinoma segmentation via uncertainty rectified pyramid consistency
X Luo, W Liao, J Chen, T Song, Y Chen, S Zhang, N Chen, G Wang, ...
MICCAI2021, 318-329, 2021
2442021
Semi-supervised medical image segmentation via cross teaching between cnn and transformer
X Luo, M Hu, T Song, G Wang, S Zhang
International conference on medical imaging with deep learning, 820-833, 2022
2392022
Semi-supervised medical image segmentation via uncertainty rectified pyramid consistency
X Luo, G Wang, W Liao, J Chen, T Song, Y Chen, S Zhang, DN Metaxas, ...
Medical Image Analysis 80, 102517, 2022
1982022
Medical image segmentation using squeeze-and-expansion transformers
S Li, X Sui, X Luo, X Xu, Y Liu, R Goh
IJCAI2021, 807-815, 2021
1862021
WORD: A large scale dataset, benchmark and clinical applicable study for abdominal organ segmentation from CT image
X Luo, W Liao, J Xiao, T Song, X Zhang, K Li, DN Metaxas, G Wang, ...
Medical Image Analysis 82, 102642, 2022
132*2022
MIDeepSeg: Minimally interactive segmentation of unseen objects from medical images using deep learning
X Luo, G Wang, T Song, J Zhang, M Aertsen, J Deprest, S Ourselin, ...
Medical image analysis 72, 102102, 2021
1072021
Scribble-Supervised Medical Image Segmentation via Dual-Branch Network and Dynamically Mixed Pseudo Labels Supervision
X Luo, M Hu, W Liao, S Zhai, T Song, G Wang, S Zhang
MICCAI2022 (early accepted & student travel award), 528–538, 2022
1012022
TransUNet: Rethinking the U-Net architecture design for medical image segmentation through the lens of transformers
J Chen, J Mei, X Li, Y Lu, Q Yu, Q Wei, X Luo, Y Xie, E Adeli, Y Wang, ...
Medical Image Analysis 97, 103280, 2024
91*2024
SCPM-Net: An Anchor-free 3D Lung Nodule Detection Network using Sphere Representation and Center Points Matching
X Luo, T Song, G Wang, J Chen, Y Chen, K Li, DN Metaxas, S Zhang
Medical Image Analysis 75, 102287, 2022
752022
Ambiguity-selective consistency regularization for mean-teacher semi-supervised medical image segmentation
Z Xu, Y Wang, D Lu, X Luo, J Yan, Y Zheng, RK Tong
Medical Image Analysis 88, 102880, 2023
672023
SSL4MIS
X Luo
https://github.com/HiLab-git/SSL4MIS, 2020
522020
Fully Test-Time Adaptation for Image Segmentation
M Hu, T Song, Y Gu, X Luo, J Chen, Y Chen, Y Zhang, S Zhang
MICCAI2021, 251-260, 2021
432021
PyMIC: A deep learning toolkit for annotation-efficient medical image segmentation
G Wang, X Luo, R Gu, S Yang, Y Qu, S Zhai, Q Zhao, K Li, S Zhang
Computer Methods and Programs in Biomedicine 231, 107398, 2023
352023
CPM-Net: A 3d center-points matching network for pulmonary nodule detection in ct scans
T Song, J Chen, X Luo, Y Huang, X Liu, N Huang, Y Chen, Z Ye, H Sheng, ...
MICCAI2020 (early accepted), 550-559, 2020
352020
Deep learning-based accurate delineation of primary gross tumor volume of nasopharyngeal carcinoma on heterogeneous magnetic resonance imaging: a large-scale and multi-center study
X Luo, W Liao, Y He, F Tang, M Wu, Y Shen, H Huang, T Song, K Li, ...
Radiotherapy and Oncology 180, 109480, 2023
312023
PA-Seg: Learning from Point Annotations for 3D Medical Image Segmentation using Contextual Regularization and Cross Knowledge Distillation
S Zhai, G Wang, X Luo, Q Yue, K Li, S Zhang
IEEE Transactions on Medical Imaging, 2023
262023
Mis-fm: 3d medical image segmentation using foundation models pretrained on a large-scale unannotated dataset
G Wang, J Wu, X Luo, X Liu, K Li, S Zhang
arXiv preprint arXiv:2306.16925, 2023
252023
Few-Shot Domain Adaptation with Polymorphic Transformers
S Li, X Sui, J Fu, H Fu, X Luo, Y Feng, X Xu, Y Liu, DSW Ting, RSM Goh
MICCAI2021, 330-340, 2021
222021
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