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Department of Computing
Ben Glocker Professor in Machine Learning for Imaging
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Recent Papers

  • Glocker et al. (2023) Algorithmic encoding of protected characteristics in chest X-ray disease detection models
  • Monteiro et al. (2023) Measuring axiomatic soundness of counterfactual image models
  • Li et al. (2023) Context Label Learning: Improving Background Class Representations in Semantic Segmentation
  • Langley et al. (2022) Structured Uncertainty in the Observation Space of Variational Autoencoders
  • Bernhardt et al. (2022) Failure Detection in Medical Image Classification: A Reality Check and Benchmarking Testbed
  • Bernhardt et al. (2022) Potential sources of dataset bias complicate investigation of underdiagnosis by machine learning algorithms
  • Liu et al. (2022) The medical algorithmic audit
  • Bernhardt et al. (2022) Active label cleaning for improved dataset quality under resource constraints
  • Sinclair et al. (2022) Atlas-ISTN: Joint segmentation and registration and atlas construction with image-and-spatial transformer networks
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kamnitsas2017emma

2017 (1)

  • K. Kamnitsas, W. Bai, E. Ferrante, S. McDonagh, M. Sinclair, N. Pawlowski, M. Rajchl, M. Lee, B. Kainz, D. Rueckert, B. Glocker, Ensembles of Multiple Models and Architectures for Robust Brain Tumour Segmentation, MICCAI Brain Lesion Workshop, 2017
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