Publications
Journals
[J14] Li, Y. F., Ji, H. B., Chen, X., Yang, Y. L., & Lai, Y. K. (2024). Learning key lines for multi-object tracking. Computer Vision and Image Understanding , 241. [link]
[J13] Tang, T., Ryan, G., Ding, H., Chen, X., Zang, J., Taylor, P., & Adcock, T. (2023). A new Gaussian Process based model for non-linear wave loading on vertical cylinders. Coastal Engineering. [link]
[J12] Wilson, H., Chen, X., Golbabaee, M., Proulx, M. J., & O’Neill, E. (2023). Feasibility of decoding visual information from EEG. Brain-Computer Interfaces, 1-28.[pdf]
[J11] Wei, Y., Chen, X., Zhu, L., Zhang, L., Schönlieb, C. B., Price, S. J., & Li, C. (2023). Multi-modal learning for predicting the genotype of glioma. IEEE Transactions on Medical Imaging. [pdf]
[J10] Li, Y. F., Ji, H. B., Chen, X., Lai, Y. K., & Yang, Y. L. (2023). Multi-object tracking with robust object regression and association. Computer Vision and Image Understanding, 227, 103586. [Link]
[J9] Chen, X., Feroz, F., & Hobson, M. (2022). Bayesian posterior repartitioning for nested sampling. Bayesian Analysis, 1(1), 1-27. [pdf]
[J8] Ashton, G., Barbary, K., Bernstein N., Buchner J., Chen X., Csányi G., Fowlie A., et. al. (2022). Nested Sampling for physical scientists. Nature Reviews: Methods Primers , 2(1), 39. [pdf]
[J7] Gong, X., Chen, X.*, Zhong, Z., & Chen, W. (2021). Enhanced Few-Shot Learning for Intrusion Detection in Railway Video Surveillance. IEEE Transactions on Intelligent Transportation Systems, 23(8), 11301-11313. [pdf]
[J6] Rivadulla, A., Chen, X., Weir, G., Cazzola, D., Trewartha, G., Hamill, J., & Preatoni, E. (2021). Development and validation of FootNet; a new kinematic algorithm to improve foot-strike and toe-off detection in treadmill running. PLoS ONE, 16(8), e0248608. [pdf]
[J5] Das, S., Hobson, M. P., Feroz, F., Chen, X., Phadke, S., Goudswaard, J., & Hohl, D. (2021). Microseismic event detection in large heterogeneous velocity models using Bayesian multimodal nested sampling. Data-Centric Engineering, 2, e1. [pdf]
[J4] Chen, X., Hobson, M., Das, S., & Gelderblom, P. (2019). Improving the efficiency and robustness of nested sampling using posterior repartitioning. Statistics and Computing, 29, 835-850. [pdf]
[J3] Das, S., Chen, X., Hobson, M. P., et. al. (2018). Surrogate regression modelling for fast seismogram generation and detection of microseismic events in heterogeneous velocity models. Geophysical Journal International, 215(2), 1257-1290. [pdf]
[J2] Das, S., Chen, X., & Hobson, M. P. (2017). Fast GPU-Based seismogram simulation from microseismic events in marine environments using heterogeneous velocity models. IEEE Transactions on Computational Imaging, 3(2), 316-329. [pdf]
[J1] Chen, X., Särkkä, S., & Godsill, S. (2015). A Bayesian particle filtering method for brain source localisation. Digital Signal Processing, 47, 192-204. [pdf]
Conference Proceedings
[C19] Tang, T., Ding, H., Dai, S., Chen, X., Taylor, P. H., Zang, J., & Adcock, T. A. (2023). Data informed model test design with machine learning–an example in nonlinear wave load on a vertical cylinder. In International Conference on Offshore Mechanics and Arctic Engineering (OMAE) (Vol. 86878, p. V005T06A015). . [pdf]
[C18] Li, Y., Li, C., Wei, Y., Price, S., Schönlieb, C. B., & Chen, X.* (2023). G-CNN: Adaptive Geometric Convolutional Neural Networks for MRI-Based Skull Stripping. In International Workshop on Computational Mathematics Modeling in Cancer Analysis (pp. 21-30). Cham: Springer Nature Switzerland.. [pdf]
[C17] Mao, Y., Jiang, L., Chen, X., & Li, C. (2023). DisC-Diff: Disentangled Conditional Diffusion Model for Multi-Contrast MRI Super-Resolution. In International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2023). [pdf]
[C16] Jiang, L., Mao, Y., Chen, X., Wang, X., & Li, C. (2023). CoLa-Diff: Conditional Latent Diffusion Model for Multi-Modal MRI Synthesis. In International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2023). [pdf]
[C15] Li, C., Huang, W., Chen, X.*, Wei, Y., Price, S. J., & Schönlieb, C. B. (2023). Expectation-Maximization Regularized Deep Learning for Weakly Supervised Tumor Segmentation for Glioblastoma. In IEEE International Symposium on Biomedical Imaging (ISBI). [pdf]
[C14] Wei, Y., Chen, X., Schönlieb, C. B., & Price, S. J., Li, C. (2023). Predicting conversion of mild cognitive impairment to Alzheimer's disease by modelling healthy ageing trajectories. In IEEE International Symposium on Biomedical Imaging (ISBI). [pdf]
[C13] Carter, J., Chen, X., Cazzola, D., Trewartha, G. & Preatoni, E. (2023). Estimation of ground reaction force during running using consumer-level wearable insoles and machine learning. In ISBS Proceedings Archives: 41st International Conference on Biomechanics in Sports. [pdf]
[C12] Wei, Y., Li, C., Chen, X., Schönlieb, C. B., & Price, S. J. (2022). Collaborative learning of images and geometrics for predicting isocitrate dehydrogenase status of glioma. In IEEE International Symposium on Biomedical Imaging (ISBI). [pdf]
[C11] Rivadulla, A., Chen, X., Cazzola, D., Trewartha, G., & Preatoni, E. (2022). Does one single most economical running technique exist? Some preliminary results. In Abstract Book of the 9th World Congress of Biomechanics. [link]
[C10] Li, C., Wei, Y., & Chen, X.*, Schonlieb, C. B. (2021) BrainNetGAN: Data augmentation of brain connectivity using generative adversarial network for dementia classification. In Deep Generative Models, and Data Augmentation, Labelling, and Imperfections. Springer, Cham. [pdf]
[C9] Wei, Y., Li, Y., Chen, X., Schönlieb, C. B., Li, C., & Price, S. J. (2021). Predicting isocitrate dehydrogenase mutationstatus in glioma using structural brain networksand graph neural networks. In International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2021) Brain Lesion (BrainLes) workshop. [pdf]
[C8] Li, Y., Li, C., Wei, Y., Price, S., Schönlieb, C. B., & Chen, X.* (2021). Adaptive unsupervised learning with enhanced feature representation for intra-tumor partitioning and survival prediction for glioblastoma. In International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2021) Brain Lesion (BrainLes) workshop. [pdf]
[C7] Rivadulla, A. R., Chen, X., Weir, G., Cazzola, D., Trewartha, G., Hamill, J., & Preatoni, E. (2021). Development and validation of FootNet, a new kinematic and deep learning-based algorithm to detect foot-strike and toe-off in treadmill running. In ISBS Proceedings Archives: 39th International Conference on Biomechanics in Sports. [pdf]
[C6] Ramirez, B. A., Gelderblom, P. P., Eales, A. D., Chen, X., Hobson, M. P., & Esler, K. (2017). Sampling From the Posterior in Reservoir Simulation. In Abu Dhabi International Petroleum Exhibition (ADIPE) & Conference. Society of Petroleum Engineers. [link]
[C5] Chen, X., Särkkä, S., & Godsill, S. (2013). Probabilistic initiation and termination for MEG multiple dipole localization using sequential Monte Carlo methods. In Proceedings of the 16th IEEE International Conference on Information Fusion (Fusion 2013) (pp. 580-587). [link]
[C4] Chen, X., & Godsill, S. (2013). Multiple dipolar sources localization for MEG using Bayesian particle filtering. In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2013) (pp. 949-953). [link]
[C3] Edelstein, A., Chen, X., Li, Y., & Rabbat, M. (2011). RSS-based node localization in the presence of attenuating objects. In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2011) (pp. 3528-3531). [pdf]
[C2] Chen, X., Edelstein, A., Li, Y., Coates, M., Rabbat, M., & Men, A. (2011). Sequential Monte Carlo for simultaneous passive device-free tracking and sensor localization using received signal strength measurements. In Proceedings of the 10th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN 2011) (pp. 342-353). [pdf]
[C1] Li, Y., Chen, X.*, Coates, M., & Yang, B. (2011). Sequential Monte Carlo radio-frequency tomographic tracking. In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2011) (pp. 3976-3979). [pdf]
Book chapter
[B1] Lu, Y., Jin, Y., & Chen, X.* (2023) Recombination-based two-stage out-of-distribution detection method for traffic flow pattern analysis. In Handbook on Artificial Intelligence and Transport. Edward Elgar. [link]
* Equal first or corresponding authorships.
