徐昊，本硕博均就读于牛津大学。2020—2023年被英国伦敦大学国王学院人工智能中心聘为副研究员，做博士后研究。主要研究方向为心脏结构及功能分析、医疗图像处理与分析。目前在国际知名期刊和会议上发表论文11篇，其中SCI检索4篇，EI检索7篇。

主要论文：

1.Xu, H., Williams, S., Williams, M., Newby, D. E., Taylor, J., Neji, R., ... & Young, A. (2023). Deep Learning Estimation of Three-Dimensional Left Atrial Shape from Two-Chamber and Four-Chamber Cardiac Long Axis Views. European Heart Journal Cardiovascular Imaging.

2.Deng, Y., Wen, Y., Qian, L., Puyol Anton, E., Xu, H., Pushparajah, K., ... & Young, A. (2023, January). Multi-modal Latent-Space Self-alignment for Super Resolution Cardiac MR Segmentation. In Statistical Atlases and Computational Models of the Heart. Regular and CMRxMotion Challenge Papers: 13th International Workshop, STACOM 2022, Held in Conjunction with MICCAI 2022, Singapore, September 18, 2022, Revised Selected Papers (pp. 26-35). Cham: Springer Nature Switzerland.

3.Muffoletto, M., Xu, H., Barbaroux, H., Kunze, K. P., Neji, R., Botnar, R., ... & Young, A. (2023, January). Comparison of Semi-and Un-Supervised Domain Adaptation Methods for Whole-Heart Segmentation. In Statistical Atlases and Computational Models of the Heart. Regular and CMRxMotion Challenge Papers: 13th International Workshop, STACOM 2022, Held in Conjunction with MICCAI 2022, Singapore, September 18, 2022, Revised Selected Papers (pp. 91-100). Cham: Springer Nature Switzerland.

4.Sahota, M., Saraskani, S. R., Xu, H., Li, L., Majeed, A. W., Hermida, U., ... & HCMR investigators. (2022). Machine learning evaluation of LV outflow obstruction in hypertrophic cardiomyopathy using three-chamber cardiovascular magnetic resonance. The International Journal of Cardiovascular Imaging, 38(12), 2695-2705.

5.Xu, H., Niederer, S. A., Williams, S. E., Newby, D. E., Williams, M. C., & 

Young, A. A. (2021, June). Whole Heart Anatomical Refinement from CCTA 

Using Extrapolation and Parcellation. In International Conference on 

Functional Imaging and Modeling of the Heart (pp. 63-70). Springer, Cham.

 6.Xu, H. (2020). Anatomical and functional analysis of cardiac MRI scans using deep learning [PhD thesis]. University of Oxford.2020 Blonder, B., Both, S., Jodra, M., Xu, H., Fricker, M., Matos, I. S., ... & Malhi, Y. (2020). Linking functional traits to multiscale statistics of leaf venation networks. New Phytologist, 228(6), 1796-1810.

7.Xu, H., Blonder, B., Jodra, M., Malhi, Y., & Fricker, M. (2021). Automated and accurate segmentation of leaf venation networks via deep learning. New Phytologist, 229(1), 631-648.

8.Acero, J. C.*, Xu, H.*, Zacur, E., Schneider, J. E., Lamata, P., Bueno-Orovio, A., & Grau, V. (2019, October). Left Ventricle Quantification with Cardiac MRI: Deep Learning Meets Statistical Models of Deformation. In International 

Workshop on Statistical Atlases and Computational Models of the Heart. 

Springer, Cham. (* co-first authors)

9.Xu, H., Zacur, E., Schneider, J. E., & Grau, V. (2019, June). Ventricle Surface Reconstruction from Cardiac MR Slices Using Deep Learning. In International Conference on Functional Imaging and Modeling of the Heart (pp. 342-351). 

Springer, Cham.

10. Acero, J. C., Zacur, E., Xu, H., Ariga, R., Bueno-Orovio, A., Lamata, P., & Grau, V. (2019, June). SMOD-Data Augmentation Based on Statistical Models of Deformation to Enhance Segmentation in 2D Cine Cardiac MRI. In International Conference on Functional Imaging and Modeling of the Heart (pp. 361-369). Springer, Cham.

11.Xu, H., Schneider, J. E., & Grau, V. (2018, September). Calculation of 

Anatomical and Functional Metrics Using Deep Learning in Cardiac MRI: 

Comparison Between Direct and Segmentation-Based Estimation. In International Workshop on Statistical Atlases and Computational Models of 

the Heart (pp. 402-411). Springer, Cham.