University of Bamberg
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Information Systems and Applied Computer Sciences

Chair of Explainable Machine Learning

  1.  University of bamberg
  2. Faculties
  3. Information Systems and Applied Computer Sciences
  4. Subject groups
  5. Applied Computer Science
  6. Chair of Explainable Machine Learning
  7. Publications

All publications

Selected publications

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. I?gum “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]