Ben Glocker
Professor in Machine Learning for Imaging
Develops machine learning for medical image analysis, including causal and fair medical imaging AI and safe deployment of clinical models.
Ben Glocker's lab at Imperial works on medical imaging & healthcare AI. 95 papers since 2023, 34 at top venues, mostly MICCAI and ICML. 8 PhD students. 36 alumni. Applications close .
At a glance
- papers, 2023–2026
- 25/ year
- at T1 venues
- 36%
- PhD students
- 8
- MS students
- 0
- alumni listed
- 36
Papers by yearfrom the lab homepage, 2023–2026
- 19 papers2026 · T1 4 · collecting
- 28 papers2025 · T1 13
- 18 papers2024 · T1 8
- 30 papers2023 · T1 9
T1 venuesOtherVenues: MICCAI, ICML, ISBI, Medical Image Analysis, ICLR
Research focuspaper titles that use each topic's words
- Medical imaging & healthcare AI21 papers
- medical14 papers
- fair3 papers
- medical imaging3 papers
- clinical3 papers
Paper list
From the lab homepage.
202619 papers
- TMI (T1)Average Calibration Losses for Reliable Uncertainty in Medical Image Segmentation
- Deep-BreathBenchmarking the Robustness of Foundation Models for Mammography under Domain Shift
- IEEE International Conference on E-health Networking, Application & ServicesClassical MRI Pipelines Rival Deep Learning for Dementia Classification: A Benchmarking Study
- BRIDGECounterfactual Stress Testing for Image Classification Models
- The Lancet Digital HealthDevelopment of a target product profile for artificial intelligence in diabetic eye screening in England: a modified Delphi consensus study
- Nature (T1)Disparate privacy risks from medical AI
- ICML (T1)Factored Classifier-Free Guidance (opens in a new tab)
- MLCNFew-Shot Cross-Site Domain Generalization for Multi-Site Autism Brain Network Classification
- SASHIMIFew-Shot Flow Matching with Stochastic Barycentric Sampling for Image Synthesis
- STACOMGRC-ProbNet: Uncertainty-aware Feature Extraction for Cardiovascular Disease Classification
- BMJ Digital Health & AIIdentifying and understanding significant change due to drift when assessing AI models in healthcare: a narrative review
- MICCAI (T1)Latent-to-Latent Flow for Volumetric Stochastic Segmentation
202528 papers
- STACOMA comprehensive Pipeline for Aortic Segmentation and Shape Analysis
- BMJ Health & Care InformaticsAssessing artificial intelligence in breast screening with stratified results on 306 839 mammograms across geographic regions, age, breast density and ethnicity: A Retrospective Investigation Evaluating Screening (ARIES) study
- MICCAI (T1)Automatic dataset shift identification to support safe deployment of medical imaging AI
- Computers in Biology and MedicineCalibration and Uncertainty for multiRater Volume Assessment in multiorgan Segmentation (CURVAS) challenge results
- STACOMCardiovascular disease classification using radiomics and geometric features from cardiac CT Images
- FAIMICausal Representation Learning with Observational Grouping for CXR Classification
- MICCAI (T1)CF-Seg: Counterfactuals meet Segmentation
- ICML (T1)Continuous Bayesian Model Selection for Multivariate Causal Discovery
- NeurIPS (T1)Counterfactual Identifiability via Dynamic Optimal Transport
- CAPIDelineation Uncertainty from Clinician Ranges in Cervical Cancer Radiotherapy Planning
- Foundations and Trends in Computer Graphics and VisionDemystifying Variational Diffusion Models (opens in a new tab)
- ICML (T1)Diffusion Counterfactual Generation with Semantic Abduction
202418 papers
- Nature Machine Intelligence (T1)A causal perspective on dataset bias in machine learning for medical imaging
- Medical Image Analysis (T1)A geometric approach to robust medical image segmentation
- MICCAI (T1)Average Calibration Error: A Differentiable Loss for Improved Reliability in Image Segmentation
- DEMICounterfactual contrastive learning: robust representations via causal image synthesis
- Insights ImagingCuration of myeloma observational study MALIMAR using XNAT: solving the challenges posed by real-world data
- Discovery ScienceExplaining Image Classifiers with Visual Debates
- Communications MedicineGeneralisable deep learning method for mammographic density prediction across imaging techniques and self-reported race
- ICLR (T1)Grounded Object-Centric Learning (opens in a new tab)
- NeurIPS (T1)Identifiable Object-Centric Representation Learning via Probabilistic Slot Attention
- ISBIImage-to-Tree with Recursive Prompting
- Nature Methods (T1)Metrics reloaded: recommendations for image analysis validation
- MICCAI (T1)Mitigating attribute amplification in counterfactual image generation
202330 papers
- Clinical OncologyA Review of the Metrics used to Assess Auto-contouring Systems in Radiotherapy
- eBioMedicineAlgorithmic encoding of protected characteristics in chest X-ray disease detection models
- FAIMIAnalysing race and sex bias in brain age prediction
- Journal of Breast ImagingArtificial Intelligence as Supporting Reader in Breast Screening: A Novel Workflow to Preserve Quality and Reduce Workload
- Nature Communications (T1)Automatic correction of performance drift under acquisition shift in medical image classification
- European Journal of Radiology OpenAutomatic localisation and per-region quantification of traumatic brain injury on head CT using atlas mapping
- Investigative RadiologyBetter Together: Data Harmonization and Cross-Study Analysis of Abdominal MRI Data From UK Biobank and the German National Cohort
- IEEE Transactions on Automation Science and EngineeringConfidence-Aware Paced-Curriculum Learning by Label Smoothing for Surgical Scene Understanding
- TMI (T1)Context Label Learning: Improving Background Class Representations in Semantic Segmentation
- Medical Image Analysis (T1)CrossMoDA 2021 challenge: Benchmark of cross-modality domain adaptation techniques for vestibular schwannoma and cochlea segmentation
- Medical Image Analysis (T1)Data synthesis and adversarial networks: A review and meta-analysis in cancer imaging (opens in a new tab)
- Investigative RadiologyDevelopment and Evaluation of Machine Learning in Whole-Body Magnetic Resonance Imaging for Detecting Metastases in Patients With Lung or Colon Cancer: A Diagnostic Test Accuracy Study
Where alumni went36 listed
Apply to Imperial2027 (Apr/Oct)
Computing PhD
- Round
- Round 2 (1 Dec 2026)
- Opens
- —
- GRE
- See program page
- English
- See program page
- Fee
- none
Confirmed on the program page.
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