Items where Author is "Qiu, H."
Article
Chen, C., Qin, C., Ouyang, C. , Li, Z., Wang, S., Qiu, H., Chen, L., Tarroni, G. 
ORCID: 0000-0002-0341-6138, Bai, W. & Rueckert, D. (2022).
Enhancing MR image segmentation with realistic adversarial data augmentation.
Medical Image Analysis, 82,
102597.
doi: 10.1016/j.media.2022.102597
Zimmerer, D., Full, P. M, Isensee, F. , Jäger, P., Adler, T., Petersen, J., Kohler, G., Ross, T., Reinke, A., Kascenas, A., Jensen, B. S., O'Neil, A. Q., Tan, J., Hou, B., Batten, J., Qiu, H., Kainz, B., Shvetsova, N., Fedulova, I., Dylov, D. V., Yu, B., Zhai, J., Hu, J., Si, R., Zhou, S., Wang, S., Li, X., Chen, X., Zhao, Y., Marimont, S. N., Tarroni, G. ORCID: 0000-0002-0341-6138, Saase, V., Maier-Hein, L. & Maier-Hein, K. (2022).
MOOD 2020: A public Benchmark for Out-of-Distribution Detection and Localization on medical Images.
IEEE Transactions on Medical Imaging, 41(10),
pp. 2728-2738.
doi: 10.1109/TMI.2022.3170077
Conference or Workshop Item
Chen, C., Qin, C., Qiu, H. , Ouyang, C., Wang, S., Chen, L., Tarroni, G. ORCID: 0000-0002-0341-6138, Bai, W. & Rueckert, D. (2020).
Realistic Adversarial Data Augmentation for MR Image Segmentation.
Paper presented at the 23rd International Conference on Medical Image Computing and Computer Assisted Intervention, 04 - 08 October 2020, Lima, Peru.
doi: 10.1007/978-3-030-59710-8_65
Chen, C., Ouyang, C., Tarroni, G. ORCID: 0000-0002-0341-6138 , Schlemper, J., Qiu, H., Bai, W. & Rueckert, D. (2020).
Unsupervised Multi-modal Style Transfer for Cardiac MR Segmentation.
In: Pop, M., Sermesant, M., Camara, O. , Zhuang, X., Li, S., Young, A., Mansi, T. & Suinesiaputra, A. (Eds.),
Statistical Atlases and Computational Models of the Heart. Multi-Sequence CMR Segmentation, CRT-EPiggy and LV Full Quantification Challenges. STACOM 2019.
(pp. 209-219). Cham, Switzerland: Springer.
ISBN 978-3-030-39073-0
doi: 10.1007/978-3-030-39074-7_22
Working Paper
Chen, C., Qin, C., Qiu, H. , Tarroni, G. ORCID: 0000-0002-0341-6138, Duan, J., Bai, W. & Rueckert, D. (2019).
Deep learning for cardiac image segmentation: A review.
City, university of London.