Principal Investigator: Ben Glocker
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
Publications
2022 (1)
- Matthew Sinclair, Andreas Schuh, Karl Hahn, Kersten Petersen, Ying Bai, James Batten, Michiel Schaap, Ben Glocker, Atlas-ISTN: Joint segmentation, registration and atlas construction with image-and-spatial transformer networks, Medical Image Analysis, 2022
2019 (4)
- M Lee, K Petersen, N Pawlowski, B Glocker, M Schaap, Template Transformer Networks for Image Segmentation, International Conference on Medical Imaging with Deep Learning (MIDL), abstract track, non-archival, 2019
- Matt Lee, Ozan Oktay, Andreas Schuh, Michiel Schaap, Ben Glocker, Image-and-Spatial Transformer Networks for Structure-Guided Image Registration, International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), 2019
- Lee M, Petersen K, Pawlowski N, Glocker B, Schaap M, TeTrIS: Template Transformer Networks for Image Segmentation with Shape Priors, IEEE Transactions on Medical Imaging, 2019
- Jo Schlemper, Ozan Oktay, Michiel Schaap, Mattias Heinrich, Bernhard Kainz, Ben Glocker, Daniel Rueckert, Attention Gated Networks: Learning to Leverage Salient Regions in Medical Images, Medical Image Analysis, 2019