@article{li2024implicit,title={Implicit In-context Learning},author={Li, Zhuowei and Xu, Zihao and Han, Ligong and Gao, Yunhe and Wen, Song and Liu, Di and Wang, Hao and Metaxas, Dimitris N},journal={arXiv preprint arXiv:2405.14660},year={2024},}
Steering prototypes with prompt-tuning for rehearsal-free continual learning
Zhuowei Li, Long Zhao, Zizhao Zhang, Han Zhang, Di Liu, Ting Liu, and Dimitris N Metaxas
In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2024
@inproceedings{li2024steering,title={Steering prototypes with prompt-tuning for rehearsal-free continual learning},author={Li, Zhuowei and Zhao, Long and Zhang, Zizhao and Zhang, Han and Liu, Di and Liu, Ting and Metaxas, Dimitris N},booktitle={Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision},pages={2523--2533},year={2024},}
Training Like a Medical Resident: Context-Prior Learning Toward Universal Medical Image Segmentation
Yunhe Gao, Zhuowei Li, Di Liu, Mu Zhou, Shaoting Zhang, and DN Meta
In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024
@inproceedings{Gao_2024_CVPR,author={Gao, Yunhe and Li, Zhuowei and Liu, Di and Zhou, Mu and Zhang, Shaoting and Meta, DN},title={Training Like a Medical Resident: Context-Prior Learning Toward Universal Medical Image Segmentation},booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},year={2024},pages={11194-11204},}
2023
Deformer: Integrating transformers with deformable models for 3d shape abstraction from a single image
Di Liu, Xiang Yu, Meng Ye, Qilong Zhangli, Zhuowei Li, Zhixing Zhang, and Dimitris N Metaxas
In Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023
@inproceedings{liu2023deformer,title={Deformer: Integrating transformers with deformable models for 3d shape abstraction from a single image},author={Liu, Di and Yu, Xiang and Ye, Meng and Zhangli, Qilong and Li, Zhuowei and Zhang, Zhixing and Metaxas, Dimitris N},booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},pages={14236--14246},year={2023},}
2022
Towards self-supervised and weight-preserving neural architecture search
Zhuowei Li, Yibo Gao, Zhenzhou Zha, Zhiqiang Hu, Qing Xia, Shaoting Zhang, and Dimitris N Metaxas
@inproceedings{li2022towards,title={Towards self-supervised and weight-preserving neural architecture search},author={Li, Zhuowei and Gao, Yibo and Zha, Zhenzhou and Hu, Zhiqiang and Xia, Qing and Zhang, Shaoting and Metaxas, Dimitris N},booktitle={European Conference on Computer Vision},pages={3--19},year={2022},organization={Springer},}
Contrastive and selective hidden embeddings for medical image segmentation
@article{liu2022contrastive,title={Contrastive and selective hidden embeddings for medical image segmentation},author={Liu, Zihao and Li, Zhuowei and Hu, Zhiqiang and Xia, Qing and Xiong, Ruiqin and Zhang, Shaoting and Jiang, Tingting},journal={IEEE Transactions on Medical Imaging},volume={41},number={11},pages={3398--3410},year={2022},publisher={IEEE},}
2021
A deep reinforced tree-traversal agent for coronary artery centerline extraction
In Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part V 24, 2021
@inproceedings{li2021deep,title={A deep reinforced tree-traversal agent for coronary artery centerline extraction},author={Li, Zhuowei and Xia, Qing and Hu, Zhiqiang and Wang, Wenji and Xu, Lijian and Zhang, Shaoting},booktitle={Medical Image Computing and Computer Assisted Intervention--MICCAI 2021: 24th International Conference, Strasbourg, France, September 27--October 1, 2021, Proceedings, Part V 24},pages={418--428},year={2021},organization={Springer},}
Self-ensembling contrastive learning for semi-supervised medical image segmentation
@article{xiang2021self,title={Self-ensembling contrastive learning for semi-supervised medical image segmentation},author={Xiang, Jinxi and Li, Zhuowei and Wang, Wenji and Xia, Qing and Zhang, Shaoting},journal={arXiv preprint arXiv:2105.12924},year={2021},}
Few-shot learning by a cascaded framework with shape-constrained pseudo label assessment for whole heart segmentation
Wenji Wang, Qing Xia, Zhiqiang Hu, Zhennan Yan, Zhuowei Li, Yang Wu, Ning Huang, Yue Gao, Dimitris Metaxas, and Shaoting Zhang
@article{wang2021few,title={Few-shot learning by a cascaded framework with shape-constrained pseudo label assessment for whole heart segmentation},author={Wang, Wenji and Xia, Qing and Hu, Zhiqiang and Yan, Zhennan and Li, Zhuowei and Wu, Yang and Huang, Ning and Gao, Yue and Metaxas, Dimitris and Zhang, Shaoting},journal={IEEE Transactions on Medical Imaging},volume={40},number={10},pages={2629--2641},year={2021},publisher={IEEE},}
2020
Segmentation to label: automatic coronary artery labeling from mask parcellation
In Machine Learning in Medical Imaging: 11th International Workshop, MLMI 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, Proceedings 11, 2020
@inproceedings{li2020segmentation,title={Segmentation to label: automatic coronary artery labeling from mask parcellation},author={Li, Zhuowei and Xia, Qing and Wang, Wenji and Yan, Zhennan and Yin, Ruohan and Pan, Changjie and Metaxas, Dimitris},booktitle={Machine Learning in Medical Imaging: 11th International Workshop, MLMI 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, Proceedings 11},pages={130--138},year={2020},organization={Springer},}
2019
One-shot learning for function-specific region segmentation in mouse brain
Xu Zhang, Zhuowei Li*, Pei-Jie Wang, Katelyn Y Liao, Shen-Ju Chou, Shih-Fu Chang, and Jung-Chi Liao
In 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), 2019
@inproceedings{zhang2019one,title={One-shot learning for function-specific region segmentation in mouse brain},author={Zhang, Xu and Li, Zhuowei and Wang, Pei-Jie and Liao, Katelyn Y and Chou, Shen-Ju and Chang, Shih-Fu and Liao, Jung-Chi},booktitle={2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)},pages={736--740},year={2019},organization={IEEE},}
