@article{deng2024unlocking,title={Unlocking the Potential of Prompt-Tuning in Bridging Generalized and Personalized Federated Learning},author={Deng, Wenlong and Thrampoulidis, Christos and Li, Xiaoxiao},journal={Conference on Computer Vision and Pattern Recognition},year={2024},}
MedIA
LESS: Label-efficient multi-scale learning for cytological whole slide image screening
Beidi Zhao, Wenlong Deng, Zi Han Henry Li, Chen Zhou, Zuhua Gao, Gang Wang, and Xiaoxiao Li
@article{zhao2024less,title={LESS: Label-efficient multi-scale learning for cytological whole slide image screening},author={Zhao, Beidi and Deng, Wenlong and Li, Zi Han Henry and Zhou, Chen and Gao, Zuhua and Wang, Gang and Li, Xiaoxiao},journal={Medical Image Analysis},pages={103109},year={2024},publisher={Elsevier},}
SaTML
Backdoor Attack on Unpaired Medical Image-Text Foundation Models: A Pilot Study on MedCLIP
Ruinan Jin, Chun-Yin Huang, Chenyu You, and Xiaoxiao Li
2nd IEEE Conference on Secure and Trustworthy Machine Learning, 2024
@article{jin2024backdoor,title={Backdoor Attack on Unpaired Medical Image-Text Foundation Models: A Pilot Study on MedCLIP},author={Jin, Ruinan and Huang, Chun-Yin and You, Chenyu and Li, Xiaoxiao},journal={2nd IEEE Conference on Secure and Trustworthy Machine Learning},year={2024},}
ICLR
Heterogeneous Personalized Federated Learning by Local-Global Updates Mixing via Convergence Rate
Meirui Jiang, Anjie Le, Xiaoxiao Li, and Qi Dou
In The Twelfth International Conference on Learning Representations, 2024
@inproceedings{jiang2024heterogeneous,title={Heterogeneous Personalized Federated Learning by Local-Global Updates Mixing via Convergence Rate},author={Jiang, Meirui and Le, Anjie and Li, Xiaoxiao and Dou, Qi},booktitle={The Twelfth International Conference on Learning Representations},year={2024},}
2023
ICML
Federated adversarial learning: A framework with convergence analysis
Xiaoxiao Li, Zhao Song, and Jiaming Yang
In International Conference on Machine Learning, 2023
@inproceedings{li2023federated,title={Federated adversarial learning: A framework with convergence analysis},author={Li, Xiaoxiao and Song, Zhao and Yang, Jiaming},booktitle={International Conference on Machine Learning},pages={19932--19959},year={2023},organization={PMLR},}
Nature Methods
BUDDY: molecular formula discovery via bottom-up MS/MS interrogation
Shipei Xing, Sam Shen, Banghua Xu, Xiaoxiao Li, and Tao Huan
@article{xing2023buddy,title={BUDDY: molecular formula discovery via bottom-up MS/MS interrogation},author={Xing, Shipei and Shen, Sam and Xu, Banghua and Li, Xiaoxiao and Huan, Tao},journal={Nature Methods},pages={1--10},year={2023},publisher={Nature Publishing Group US New York},}
JBHI
Dynamic Corrected Split Federated Learning with Homomorphic Encryption for U-shaped Medical Image Networks
Ziyuan Yang, Yingyu Chen, Huijie Huangfu, Maosong Ran, Hui Wang, Xiaoxiao Li, and Yi Zhang
IEEE Journal of Biomedical and Health Informatics, 2023
@article{yang2023dynamic,title={Dynamic Corrected Split Federated Learning with Homomorphic Encryption for U-shaped Medical Image Networks},author={Yang, Ziyuan and Chen, Yingyu and Huangfu, Huijie and Ran, Maosong and Wang, Hui and Li, Xiaoxiao and Zhang, Yi},journal={IEEE Journal of Biomedical and Health Informatics},year={2023},publisher={IEEE},}
MedIA
Backdoor attack and defense in federated generative adversarial network-based medical image synthesis
@article{jin2023backdoor,title={Backdoor attack and defense in federated generative adversarial network-based medical image synthesis},author={Jin, Ruinan and Li, Xiaoxiao},journal={Medical Image Analysis},pages={102965},year={2023},publisher={Elsevier},}
MICCAI
FedSoup: Improving Generalization and Personalization in Federated Learning via Selective Model Interpolation
Minghui Chen, Meirui Jiang, Qi Dou, Zehua Wang, and Xiaoxiao Li
International Conference on Medical Image Computing and Computer-Assisted Intervention, 2023
@article{chen2023fedsoup,title={FedSoup: Improving Generalization and Personalization in Federated Learning via Selective Model Interpolation},author={Chen, Minghui and Jiang, Meirui and Dou, Qi and Wang, Zehua and Li, Xiaoxiao},journal={International Conference on Medical Image Computing and Computer-Assisted Intervention},year={2023},}
MICCAI
Community-Aware Transformer for Autism Prediction in fMRI Connectome
Anushree Bannadabhavi, Soojin Lee, Wenlong Deng, and Xiaoxiao Li
@article{bannadabhavi2023community,title={Community-Aware Transformer for Autism Prediction in fMRI Connectome},author={Bannadabhavi, Anushree and Lee, Soojin and Deng, Wenlong and Li, Xiaoxiao},journal={arXiv preprint arXiv:2307.10181},year={2023},}
IPMI
SADM: Sequence-Aware Diffusion Model for Longitudinal Medical Image Generation
Jee Seok Yoon, Chenghao Zhang, Heung-Il Suk, Jia Guo, and Xiaoxiao Li
In International Conference on Information Processing in Medical Imaging, 2023
@inproceedings{yoon2023sadm,title={SADM: Sequence-Aware Diffusion Model for Longitudinal Medical Image Generation},author={Yoon, Jee Seok and Zhang, Chenghao and Suk, Heung-Il and Guo, Jia and Li, Xiaoxiao},booktitle={International Conference on Information Processing in Medical Imaging},pages={388--400},year={2023},organization={Springer},}
IPMI
On Fairness of Medical Image Classification with Multiple Sensitive Attributes via Learning Orthogonal Representations
Wenlong Deng, Yuan Zhong, Qi Dou, and Xiaoxiao Li
In International Conference on Information Processing in Medical Imaging, 2023
@inproceedings{deng2023fairness,title={On Fairness of Medical Image Classification with Multiple Sensitive Attributes via Learning Orthogonal Representations},author={Deng, Wenlong and Zhong, Yuan and Dou, Qi and Li, Xiaoxiao},booktitle={International Conference on Information Processing in Medical Imaging},pages={158--169},year={2023},organization={Springer},}
ICLR
PerFedMask: Personalized Federated Learning with Optimized Masking Vectors
Mehdi Setayesh, Xiaoxiao Li, and Vincent WS Wong
In The Eleventh International Conference on Learning Representations, 2023
@inproceedings{setayesh2022perfedmask,title={PerFedMask: Personalized Federated Learning with Optimized Masking Vectors},author={Setayesh, Mehdi and Li, Xiaoxiao and Wong, Vincent WS},booktitle={The Eleventh International Conference on Learning Representations},year={2023},}
@incollection{dvornek2023deep,title={Deep learning with connectomes},author={Dvornek, Nicha C and Li, Xiaoxiao},booktitle={Connectome Analysis},pages={289--308},year={2023},publisher={Elsevier},}
MedIA
Guidelines and evaluation of clinical explainable AI in medical image analysis
Weina Jin, Xiaoxiao Li, Mostafa Fatehi, and Ghassan Hamarneh
@article{jin2023guidelines,title={Guidelines and evaluation of clinical explainable AI in medical image analysis},author={Jin, Weina and Li, Xiaoxiao and Fatehi, Mostafa and Hamarneh, Ghassan},journal={Medical Image Analysis},volume={84},pages={102684},year={2023},publisher={Elsevier},}
2022
TMI
GATE: graph CCA for temporal SElf-supervised learning for label-efficient fMRI analysis
Liang Peng, Nan Wang, Jie Xu, Xiaofeng Zhu, and Xiaoxiao Li
@article{peng2022gate,title={GATE: graph CCA for temporal SElf-supervised learning for label-efficient fMRI analysis},author={Peng, Liang and Wang, Nan and Xu, Jie and Zhu, Xiaofeng and Li, Xiaoxiao},journal={IEEE Transactions on Medical Imaging},volume={42},number={2},pages={391--402},year={2022},publisher={IEEE},}
TMI
A Dataset Auditing Method for Collaboratively Trained Machine Learning Models
Yangsibo Huang, Chun-Yin Huang, Xiaoxiao Li, and Kai Li
@article{huang2022dataset,title={A Dataset Auditing Method for Collaboratively Trained Machine Learning Models},author={Huang, Yangsibo and Huang, Chun-Yin and Li, Xiaoxiao and Li, Kai},journal={IEEE Transactions on Medical Imaging},year={2022},publisher={IEEE},}
IEEE BigData
A convergence theory for federated average: Beyond smoothness
Xiaoxiao Li, Zhao Song, Runzhou Tao, and Guangyi Zhang
In 2022 IEEE International Conference on Big Data (Big Data), 2022
@inproceedings{li2022convergence,title={A convergence theory for federated average: Beyond smoothness},author={Li, Xiaoxiao and Song, Zhao and Tao, Runzhou and Zhang, Guangyi},booktitle={2022 IEEE International Conference on Big Data (Big Data)},pages={1292--1297},year={2022},organization={IEEE},}
ICCV
Exploring Resolution and Degradation Clues as Self-supervised Signal for Low Quality Object Detection
Ziteng Cui, Yingying Zhu, Lin Gu, Guo-Jun Qi, Xiaoxiao Li, Renrui Zhang, Zenghui Zhang, and Tatsuya Harada
@inproceedings{cui2022exploring,title={Exploring Resolution and Degradation Clues as Self-supervised Signal for Low Quality Object Detection},author={Cui, Ziteng and Zhu, Yingying and Gu, Lin and Qi, Guo-Jun and Li, Xiaoxiao and Zhang, Renrui and Zhang, Zenghui and Harada, Tatsuya},booktitle={European Conference on Computer Vision},pages={473--491},year={2022},organization={Springer},}
MICCAI
Dynamic bank learning for semi-supervised federated image diagnosis with class imbalance
@inproceedings{jiang2022dynamic,title={Dynamic bank learning for semi-supervised federated image diagnosis with class imbalance},author={Jiang, Meirui and Yang, Hongzheng and Li, Xiaoxiao and Liu, Quande and Heng, Pheng-Ann and Dou, Qi},booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},pages={196--206},year={2022},organization={Springer},}
MICCAI
Class Impression for Data-Free Incremental Learning
Sana Ayromlou, Purang Abolmaesumi, Teresa Tsang, and Xiaoxiao Li
In International Conference on Medical Image Computing and Computer-Assisted Intervention, 2022
@inproceedings{ayromlou2022class,title={Class Impression for Data-Free Incremental Learning},author={Ayromlou, Sana and Abolmaesumi, Purang and Tsang, Teresa and Li, Xiaoxiao},booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},pages={320--329},year={2022},organization={Springer},}
TMI
Fedni: Federated graph learning with network inpainting for population-based disease prediction
Liang Peng, Nan Wang, Nicha Dvornek, Xiaofeng Zhu, and Xiaoxiao Li
@article{peng2022fedni,title={Fedni: Federated graph learning with network inpainting for population-based disease prediction},author={Peng, Liang and Wang, Nan and Dvornek, Nicha and Zhu, Xiaofeng and Li, Xiaoxiao},journal={IEEE Transactions on Medical Imaging},year={2022},publisher={IEEE},}
ICLR
Federated learning from only unlabeled data with class-conditional-sharing clients
Nan Lu, Zhao Wang, Xiaoxiao Li, Gang Niu, Qi Dou, and Masashi Sugiyama
International Conference on Learning Representations, 2022
@article{lu2022federated,title={Federated learning from only unlabeled data with class-conditional-sharing clients},author={Lu, Nan and Wang, Zhao and Li, Xiaoxiao and Niu, Gang and Dou, Qi and Sugiyama, Masashi},journal={International Conference on Learning Representations},year={2022},}
AAAI
Evaluating explainable AI on a multi-modal medical imaging task: Can existing algorithms fulfill clinical requirements?
Weina Jin, Xiaoxiao Li, and Ghassan Hamarneh
In Proceedings of the AAAI Conference on Artificial Intelligence, 2022
@inproceedings{jin2022evaluating,title={Evaluating explainable AI on a multi-modal medical imaging task: Can existing algorithms fulfill clinical requirements?},author={Jin, Weina and Li, Xiaoxiao and Hamarneh, Ghassan},booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},volume={36},number={11},pages={11945--11953},year={2022},}
2021
ICLR
FedBN: Federated learning on non-iid features via local batch normalization
Xiaoxiao Li, Meirui Jiang, Xiaofei Zhang, Michael Kamp, and Qi Dou
International Conference on Learning Representations, 2021
@article{li2021fedbn,title={FedBN: Federated learning on non-iid features via local batch normalization},author={Li, Xiaoxiao and Jiang, Meirui and Zhang, Xiaofei and Kamp, Michael and Dou, Qi},year={2021},journal={International Conference on Learning Representations},}
ICML
Fl-NTK: A neural tangent kernel-based framework for federated learning analysis
Baihe Huang, Xiaoxiao Li, Zhao Song, and Xin Yang
In International Conference on Machine Learning, 2021
@inproceedings{huang2021fl,title={Fl-NTK: A neural tangent kernel-based framework for federated learning analysis},author={Huang, Baihe and Li, Xiaoxiao and Song, Zhao and Yang, Xin},booktitle={International Conference on Machine Learning},pages={4423--4434},year={2021},organization={PMLR},}
MedIA
BrainGNN: Interpretable brain graph neural network for fmri analysis
Xiaoxiao Li, Yuan Zhou, Nicha Dvornek, Muhan Zhang, Siyuan Gao, Juntang Zhuang, Dustin Scheinost, Lawrence H Staib, Pamela Ventola, and James S Duncan
@article{li2021braingnn,title={BrainGNN: Interpretable brain graph neural network for fmri analysis},author={Li, Xiaoxiao and Zhou, Yuan and Dvornek, Nicha and Zhang, Muhan and Gao, Siyuan and Zhuang, Juntang and Scheinost, Dustin and Staib, Lawrence H and Ventola, Pamela and Duncan, James S},journal={Medical Image Analysis},volume={74},pages={102233},year={2021},publisher={Elsevier},}
NeurIPS
Subgraph federated learning with missing neighbor generation
Ke Zhang, Carl Yang, Xiaoxiao Li, Lichao Sun, and Siu Ming Yiu
Advances in Neural Information Processing Systems, 2021
@article{zhang2021subgraph,title={Subgraph federated learning with missing neighbor generation},author={Zhang, Ke and Yang, Carl and Li, Xiaoxiao and Sun, Lichao and Yiu, Siu Ming},journal={Advances in Neural Information Processing Systems},volume={34},pages={6671--6682},year={2021},}