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2020 – today
- 2026
[j7]Xiaoqing Lin, Qianxi Jin, Taohu Zhou, Xiuxiu Zhou, Yu Guan, Xin'ang Jiang, Yi Xia, Jiong Ni, Fangyi Xu, Hongjie Hu, Shiyuan Liu, Rozemarijn Vliegenthart, Li Fan:
Nomogram based on quantitative lung CT features to identify cardiovascular disease in chronic obstructive pulmonary disease and predict prognosis. BMC Medical Imaging 26(1): 197 (2026)- [i2]Qihang Zhang, Lin Li, Luyao Zhang, Shuai Yang, Yiming Luo, Shuaiting Li, Ruilin Wang, Junke Wang, Jiahao Shao, Gangwei Xu, Jiaming Zhou, Yishu Shen, Yudong Jin, Fangyi Xu, Shuailei Ma, Jiaqi Liao, Guanxing Lu, Zifan Shi, Yongkun Wen, Yujie Zhao, Weixuan Tang, Xinyang Wang, Chaojian Li, Jiapeng Zhu, Ka Leong Cheng, Nan Xue, Xing Zhu, Yujun Shen, Yinghao Xu:
Native Video-Action Pretraining for Generalizable Robot Control. CoRR abs/2607.08639 (2026) - 2025
- [j6]Xianzhi Huang
, Fangyi Xu, Wenchao Zhu, Lin Yao, Jiahuan He, Junhao Su, Wending Zhao, Hongjie Hu:
An integrated strategy based on radiomics and quantum machine learning: diagnosis and clinical interpretation of pulmonary ground-glass nodules. BMC Medical Imaging 25(1): 279 (2025) - [j5]Rongjun Ge, Chong Wang, Yuxin Liu, Chunqiang Lu, Cong Xia, Yehui Jiang, Fangyi Xu, Yinsu Zhu, Daoqiang Zhang, Chengyu Liu, Yang Chen, Shuo Li, Yuting He:
Human gaze-based dual teacher guidance learning for semi-supervised medical image segmentation. Neural Networks 192: 107865 (2025) - [c5]Rui Xu, Huadong Liu, Yongcen Li, Xinchen Ye, Zhihui Wang, Hongkai Wang, Haojie Li, Yi Wang, Dingpin Huang, Fangyi Xu, Yi Gan, Yuan Tu, Hongjie Hu:
2.5D Top-K Ranked Multiple Instance Learning to Classify NSCLC PD-L1 Status on CT Images. ICASSP 2025: 1-5 - 2024
- [j4]Jiabin Zheng, Fangyi Xu, Wei Chen, Zihao Fang, Jiahui Yao:
Core-View Contrastive Learning Network for Building Lightweight Cross-Domain Consultation System. IEEE Access 12: 65615-65629 (2024) - [c4]Rui Xu, Dan Yu, Xuan Yang, Xinchen Ye, Zhihui Wang, Yi Wang, Hongkai Wang, Haojie Li, Dingpin Huang, Fangyi Xu, Yi Gan, Yuan Tu, Hongjie Hu:
Novelty Detection Based Discriminative Multiple Instance Feature Mining to Classify NSCLC PD-L1 Status on HE-Stained Histopathological Images. MICCAI (4) 2024: 340-350 - 2023
- [j3]Long Chen, Yingying Xu, Fangyi Xu, Qian Hu, Zhenzhou Tang:
Balancing the trade-off between cost and reliability for wireless sensor networks: a multi-objective optimized deployment method. Appl. Intell. 53(8): 9148-9173 (2023) - [j2]Long Chen, Fangyi Xu, Kezhong Jin, Zhenzhou Tang:
Energy-saving access point configurations in WLANs: a swarm intelligent approach. J. Supercomput. 79(17): 19332-19364 (2023) - [c3]Titinunt Kitrungrotsakul, Qingqing Chen, Huitao Wu, Preeyanuch Srichola, Hongjie Hu, Wenchao Zhu, Chao Chen, Fangyi Xu, Yong Zhou, Lanfen Lin, Ruofeng Tong, Jingsong Li, Yen-Wei Chen:
A hybrid model of deep learning features and clinical features for severe cases predication of COVID-19. Computer-Aided Diagnosis 2023 - 2022
- [c2]Rui Xu, Yufeng Wang, Xinchen Ye, Pengcheng Wu, Yen-Wei Chen, Fangyi Xu, Wenchao Zhu, Chao Chen, Yong Zhou, Hongjie Hu, Xiaofeng Qu, Shoji Kido, Noriyuki Tomiyama:
Pixel-Level and Affinity-Level Knowledge Distillation for Unsupervised Segmentation of Covid-19 Lesions. ICASSP 2022: 1376-1380 - [i1]Long Chen, Yingying Xu, Fangyi Xu, Qian Hu, Zhenzhou Tang:
Balancing the trade-off between cost and reliability for wireless sensor networks: a multi-objective optimized deployment method. CoRR abs/2207.09089 (2022) - 2021
- [j1]Titinunt Kitrungrotsakul, Qingqing Chen, Huitao Wu, Yutaro Iwamoto, Hongjie Hu, Wenchao Zhu, Chao Chen, Fangyi Xu, Yong Zhou, Lanfen Lin, Ruofeng Tong, Jingsong Li, Yen-Wei Chen:
Attention-RefNet: Interactive Attention Refinement Network for Infected Area Segmentation of COVID-19. IEEE J. Biomed. Health Informatics 25(7): 2363-2373 (2021) - 2020
- [c1]Rui Xu, Xiao Cao, Yufeng Wang, Yen-Wei Chen, Xinchen Ye, Lin Lin, Wenchao Zhu, Chao Chen, Fangyi Xu, Yong Zhou, Hongjie Hu, Shoji Kido, Noriyuki Tomiyama:
Unsupervised Detection of Pulmonary Opacities for Computer-Aided Diagnosis of COVID-19 on CT Images. ICPR 2020: 9007-9014
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