F. Di Salvo, D. Tafler, S. Doerrich, C. Ledig, "Privacy-preserving datasets by capturing feature distributions with Conditional VAEs", BMVC, 2024. [pdf][bib](407.0 B)[code]

S. Doerrich, F. Di Salvo, C. Ledig, "Self-supervised Vision Transformer are Scalable Generative Models for Domain Generalization", MICCAI, 2024. [doi] [pdf] [bib](2.1 KB)[code]

C. Biffi, J. J. Cerrolaza, G. Tarroni, W. Bai, A. De Marvao, O. Oktay, C. Ledig, L. Le Folgoc, K. Kamnitsas, G. Doumou, J. Duan, S. K. Prasad, S. A. Cook, D. P. O'Regan and D. Rueckert, “Explainable Anatomical Shape Analysis through Deep Hierarchical Generative Models”, IEEE Transactions on Medical Imaging, 2020. [doi] [pdf] [bib] 

J.M. Wolterink, K. Kamnitsas, C. Ledig, I. Išgum “Deep learning: Generative adversarial networks and adversarial methods?”, In: S. K. Zhou, D. Rueckert and G. Fichtinger eds., Handbook of Medical Image Computing and Computer Assisted Intervention, Academic Press, pp. 547-574, 2020. [doi] [pdf] [bib]

A. Gupta, S. Venkatesh, S. Chopra, C. Ledig, “Generative Image Translation for Data Augmentation of Bone Lesion Pathology”, accepted at MIDL, 2019. [pdf] [bib]

C. Ledig, A. Schuh, R. Guerrero, R. A. Heckemann and D. Rueckert, “Structural brain imaging in Alzheimer's disease and mild cognitive impairment: biomarker analysis and shared morphometry database”, Scientific Reports, 8, 2018. [doi] [pdf] [bib] [dataset] 

C. Ledig, L. Theis, F. Huszar, J. Caballero, A. Cunningham, A. Acosta, A. Aitken, A. Tejani, J. Totz, Z. Wang, W. Shi, “Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network”, CVPR (oral), 2017. [pdf] [bib] 

K. Kamnitsas, C. Ledig, V. F. J. Newcombe, J. P. Simpson, A. D. Kane, D. K. Menon, D. Rueckert and B. Glocker, “Efficient Multi-Scale 3D CNN with fully connected CRF for Accurate Brain Lesion Segmentation”, Medical Image Analysis, vol. 36, pp. 61-78, 2017. [pdf] [doi] [bib] [github]

C. Ledig, W. Shi, W. Bai, and D. Rueckert, “Patch-based evaluation of image segmentation”, CVPR, pp. 3065-3072, 2014. [bib] [pdf] [doi][spotlight:mpeg4][spotlight:mov] [download]