Zengqiang Yan
Orcid: 0000-0002-2039-3863
According to our database1,
Zengqiang Yan
authored at least 34 papers
between 2015 and 2024.
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Bibliography
2024
MFTrans: Modality-Masked Fusion Transformer for Incomplete Multi-Modality Brain Tumor Segmentation.
IEEE J. Biomed. Health Informatics, January, 2024
FedA3I: Annotation Quality-Aware Aggregation for Federated Medical Image Segmentation against Heterogeneous Annotation Noise.
Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024
DTMFormer: Dynamic Token Merging for Boosting Transformer-Based Medical Image Segmentation.
Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024
2023
Cluster-Re-Supervision: Bridging the Gap Between Image-Level and Pixel-Wise Labels for Weakly Supervised Medical Image Segmentation.
IEEE J. Biomed. Health Informatics, October, 2023
Affinity Feature Strengthening for Accurate, Complete and Robust Vessel Segmentation.
IEEE J. Biomed. Health Informatics, August, 2023
Pattern Recognit. Lett., August, 2023
BATFormer: Towards Boundary-Aware Lightweight Transformer for Efficient Medical Image Segmentation.
IEEE J. Biomed. Health Informatics, July, 2023
IEEE Trans. Circuits Syst. Video Technol., April, 2023
IEEE Trans. Medical Imaging, 2023
The Lighter the Better: Rethinking Transformers in Medical Image Segmentation Through Adaptive Pruning.
IEEE Trans. Medical Imaging, 2023
SAMUS: Adapting Segment Anything Model for Clinically-Friendly and Generalizable Ultrasound Image Segmentation.
CoRR, 2023
FCA: Taming Long-tailed Federated Medical Image Classification by Classifier Anchoring.
CoRR, 2023
FedIIC: Towards Robust Federated Learning for Class-Imbalanced Medical Image Classification.
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2023, 2023
ConvFormer: Plug-and-Play CNN-Style Transformers for Improving Medical Image Segmentation.
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2023, 2023
FedNoRo: Towards Noise-Robust Federated Learning by Addressing Class Imbalance and Label Noise Heterogeneity.
Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, 2023
2022
Customized Federated Learning for Multi-Source Decentralized Medical Image Classification.
IEEE J. Biomed. Health Informatics, 2022
Uncertainty-Aware Deep Learning With Cross-Task Supervision for PHE Segmentation on CT Images.
IEEE J. Biomed. Health Informatics, 2022
Symmetry-Aware Deep Learning for Cerebral Ventricle Segmentation With Intra-Ventricular Hemorrhage.
IEEE J. Biomed. Health Informatics, 2022
FedRare: Federated Learning with Intra- and Inter-Client Contrast for Effective Rare Disease Classification.
CoRR, 2022
2021
Variation-Aware Federated Learning With Multi-Source Decentralized Medical Image Data.
IEEE J. Biomed. Health Informatics, 2021
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2021
2020
Enabling a Single Deep Learning Model for Accurate Gland Instance Segmentation: A Shape-Aware Adversarial Learning Framework.
IEEE Trans. Medical Imaging, 2020
2019
IEEE J. Biomed. Health Informatics, 2019
2018
IEEE Trans. Medical Imaging, 2018
Joint Segment-Level and Pixel-Wise Losses for Deep Learning Based Retinal Vessel Segmentation.
IEEE Trans. Biomed. Eng., 2018
Describing Upper-Body Motions Based on Labanotation for Learning-from-Observation Robots.
Int. J. Comput. Vis., 2018
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2018, 2018
2017
IEEE Trans. Computational Imaging, 2017
Multim. Tools Appl., 2017
2016
Describing upper body motions based on the Labanotation for learning-from-observation robots.
CoRR, 2016
2015
Proceedings of the 17th IEEE International Workshop on Multimedia Signal Processing, 2015
Proceedings of the 2015 IEEE International Conference on Image Processing, 2015