Publications

Lafargue-Hauret, M., Mehta, R., De Sousa Ribeiro, F., Roschewitz, M. and Glocker, B. (2026)
‘Pixel-Level Counterfactual Contrastive Learning for Medical Image Segmentation.’ In: International Symposium on Biomedical Imaging (ISBI 2026).
arXiv (Cornell University). Available at: [2603.17110v1] Pixel-level Counterfactual Contrastive Learning for Medical Image Segmentation

Xia, T., Sinclair, M., Schuh, A., De Sousa Ribeiro, F., Mehta, R., Rasal, R., Puyol-Anton, E., Gerber, S., Petersen, K., Schaap, M. and Glocker, B. (2026) ‘Positional Segmentor-Guided Counterfactual Fine-Tuning for Spatially Localized Image Synthesis.’ In: International Symposium on Biomedical Imaging (ISBI 2026).arXiv (Cornell University). Available at: [PDF] Positional Segmentor-Guided Counterfactual Fine-Tuning for Spatially Localized Image Synthesis | Semantic Scholar

Elimelech, K., Yaacov, T., Kelly, D. A., Chockler, H. and Vardi, M. Y.
‘Explaining Failures of Cyber-Physical Systems with Actual Causality.’ In: IEEE International Conference on Robotics & Automation. Available at: Explaining Failures of Cyber-Physical Systems with Actual Causality - King's College London

Aslett, L. J. M., Avramescu, A., Bakewell, N., Birds, I., Bowler, L., Camilleri, M. P. J., Chung, S.-C., Clifton, D. A., Cohen, S. N., Constantine-Cooke, N., Daub, E. G., Davidson, S., Denaxas, S., Diaz-Ordaz, K., Feltbower, R., Gallier, S., Gardiner, S., Gasperoni, F., Goudie, R. J. B., Green, R. E., Hall, M., Holmes, C., Hurst, J. R., Iles, M. M., Jorge, J., Karoune, E., Keogh, R., King, R., King, R., Kirk, P. D. W., Klapaukh, R., Kouchaki, S., Lai, A. G., Lea, N., Leyrat, C., Li, K., Lilaonitkul, W., Lu, H. Y., Lyons, T., Mallon, A. M., Manderson, A., Margaritella, N., Matteson, J., Morley, S., Nicholls, H., O’Reilly, M., Pagel, C., Palmer, E., Roberts, J., Roberts, T. J., Robertson, D. S., Robinson, J., Rockenschaub, P., Ruddle, R., Sapey, E., Santos, L., Soltan, A. A. S., Smith, F. G., Starr, C., Strickson, O., Su, L., Tackney, M. S., Thygesen, J. H., Torralbo, A., Turner, A., Vallejos, C. A., Wang, C., Whitaker, K., Whitehouse, T., Westhead, D. R., Wong, W. K., Wu, Y., Yang, L. and Zou, X. (2025) ‘DECOVID: A UK two-center harmonized database of acute care electronic health records for COVID-19 research.’ Data, 10(12), 195 Available at: https://www.mdpi.com/2306-5729/10/12/195

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Butler, K., Feng, G. and Djuric, P. M. (2025) ‘Higher-Order Feature Attribution: Bridging Statistics, Explainable AI, and Topological Signal Processing.’ arXiv (Cornell University). Available at: https://arxiv.org/abs/2510.06165

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Benitez-Aurioles, J., Joules, A., Brusini, I., Peek, N. and Sperrin, M.
‘Understanding algorithmic fairness for clinical prediction in terms of subgroup net benefit and health equity.’ Epidemiology. Available at: https://journals.lww.com/epidem/abstract/9900/understanding_algorithmic_fairness_for_clinical.443.aspx

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Sanchez, P., Machlanski, D., McDonagh, S. and Tsaftaris, S. A.
‘Causal Ordering for Structure Learning from Time Series.’ In: OpenReview. Available at: https://openreview.net/forum?id=hWuTzqggSd

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Leighton, S. P., Leong, I. L., Machlanski, D., Perry, B. I., Tsaftaris, S. A., Deligianni, F., Lawrie, S. M. and Krishnadas, R. (2026) ‘Antipsychotic-induced weight gain in psychosis: causal mediation analysis and feasibility study of causal actionable prediction model development using counterfactuals to target obesity.’ The British Journal of Psychiatry. Available at: https://doi.org/10.1192/bjp.2026.10561

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Benitez-Aurioles, J., Wynants, L., Peek, N., Goodley, P., Crosbie, P. and Sperrin, M. (2026) ‘The continuous net benefit: assessing the clinical utility of prediction models when informing a continuum of decisions.’ Diagnostic and Prognostic Research. Available at: https://link.springer.com/article/10.1186/s41512-026-00224-z

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Summers, M., Sperrin, M., Vasquez Osorio, E., Banfill, K., Walls, G. and McWilliam, A. (2026) ‘Causal re‑assessment of the spatial association between normal tissue dose and overall-survival in lung radiotherapy.’ Radiotherapy and Oncology. Available at: Causal re-assessment of the spatial association between normal tissue dose and overall-survival in lung radiotherapy - ScienceDirect

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Chockler, H., Kelly, D. A., Kroening, D., & Sun, Y. (2026)‍ ‍Causal Explanations for Image Classifiers Journal of Artificial Intelligence Research Available at: https://jair.org/index.php/jair/article/view/21939/27312‍ ‍

Xia, T., De Sousa Ribeiro, F., Rasal, R. R., Kori, A., Mehta, R., & Glocker, B. (2026)‍ Factored Classifier‑Free Guidance ‍ICML 2026 Pre‑print available at: https://arxiv.org/abs/2506.14399

Yu, J., Zhou, Y., Li, H., Yu, J., Yang, M., He, Y., Zhang, N. L., Torr, P., & Silva, R. (2026)‍ Causal Fine‑Tuning under Latent Confounded Shift‍ ‍International Conference on Machine Learning (ICML 2026) Pre‑print available at: https://arxiv.org/abs/2410.14375v3

Michael P. J. Camilleri, D. Gouzou, S. Al-Wasity, M. R. K. Mookiah, M. Valdes Hernandez, B. Alex, S. A. Tsaftaris, A. Brooks, R. MacLeod, H. Wu, B. Bauer, C. Grover, P. Reel, S. Krueger, R. Tobin, J. D. Steele, G. Mair, J. Wardlaw, A. Doney, E. Trucco, & W. Whiteley (2026)‍. A large dataset of brain imaging linked to health systems data: curation and access to a whole system national cohort from NHS Scotland‍. GigaScience. Available at: DOI: https://doi.org/10.1093/gigascience/giag072.

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Kelly, D. A. and Blake, N.(2026)
Quantifying Explainable AI-introduced Signal Noise on ECG Data with Spectral Entropy. In: European Signal Processing Conference (EUSIPCO). Available at: https://arxiv.org/pdf/2606.24974

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Kelly, D. A. and Chockler, H.(2026)
I Guess That's Why They Call it the Blues: Causal Analysis for Audio Classifiers. In: ACM Multimedia. Available at: https://arxiv.org/pdf/2601.16675

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Kelly, D. A. and Chockler, H.(2026)
If It's Good Enough for You, It's Good Enough for Me: Transferability of Audio Sufficiencies across Models. In: ACM Multimedia. Available at: https://arxiv.org/pdf/2604.02937

‍Blake, N., Kelly, D. A., Kapllani-Mucaj, S., Wang, R., Thomas, G. and Chockler, H. (2026)
‘Causal responsibility based explainable AI for vibrational spectroscopy applied to oral FTIR and oesophageal Raman diagnostics.’
Scientific Reports. Available at: www.nature.com/articles

De Sousa Ribeiro, F. and Glocker, B. (2025) ‘Demystifying variational diffusion models.’ Foundations and Trends in Computer Graphics and Vision, 17(2), pp. 76–170. https://doi:10.1561/0600000113 

Dhir, A., Sedgwick, R., Kori, A., Glocker, B. and van der Wilk, M. (2025) ‘Continuous Bayesian model selection for multivariate causal discovery.’ Poster in: International Conference on Machine Learning (ICML 2025). arXiv (Cornell University). https://doi.org/10.48550/arXiv.2411.10154

Jansma, A., Yao, Y., Wolfe, J., Del Debbio, L, Beentjes, S.V., Ponting, C.P.  and Khamseh, A. (2025) 'High order expression dependencies finely resolve cryptic states and subtypes in single cell data,' Molecular Systems Biology, 21, pp. 173–207. https://doi.org/10.1038/s44320-024-00074-1

Jones, C., de Sousa Ribeiro, F., Roschewitz, M., Castro, D. C. and Glocker, B.(2024) ‘Rethinking fair representation learning for performance‑sensitive tasks.’ arXiv (Cornell University). https://doi:10.48550/arXiv.2410.04120

Roschewitz, M., De Sousa Ribeiro, F., Xia, T., Khara, G. and Glocker, B. (2025) ‘Robust image representations with counterfactual contrastive learning.’ Medical Image Analysis, 79, 102876. Robust image representations with counterfactual contrastive learning - ScienceDirect

Kori, A., Toni, F. and Glocker, B. (2025) ‘Identifiable Object Representations under Spatial Ambiguities.’ Poster in: International Conference on Machine Learning (ICML 2025). arXiv (Cornell University). https://doi.org/10.48550/arXiv.2506.07806

Rasal, R., Kori, A., De Sousa Ribeiro, F., Xia, T. and Glocker, B. (2025) ‘Diffusion counterfactual generation with semantic abduction.’ Poster in: International Conference on Machine Learning (ICML 2025). arXiv (Cornell University). https://doi.org/10.48550/arXiv.2506.07883

Kelly, D.A., Chanchal, A. and Blake, N. (2025) ‘I Am Big, You Are Little; I Am Right, You Are Wrong.’ In: International Conference on Computer Vision (ICCV 2025). arXiv (Cornell University). https://arxiv.org/pdf/2507.23509

Kelly, D.A., Chockler, H. and Blake, N. (2025) ‘Explaining Negative Classifications of AI Models in Tumor Diagnosis.’ In: Conference on Uncertainty in AI (UAI 2025). King’s College London Pure. https://kclpure.kcl.ac.uk/ws/portalfiles/portal/338736242/Explanations_of_absence.pdf

Chockler, H., Kelly, D.A. and Kroening, D.(2025) ‘Multiple Different Black Box Explanations for Image Classifiers.’ In: The European Conference on Artificial Intelligence (ECAI 2025). arXiv (Cornell University). https://arxiv.org/pdf/2309.14309

Blake, N., Kelly, D.A., Calderon Pena, S., Chanchal, A. and Chockler, H. (2025) ‘MRxaI: Black-Box Explainability for Image Classifiers in a Medical Setting.’ In: EXPLIMED Workshop, ECAI 2025. arXiv (Cornell University). https://arxiv.org/pdf/2311.14471

Blake, N., Kelly, D.A., Chanchal, A., Kapllani-Mucaj, S., Thomas, G. and Chockler, H. (2025) ‘SpecReX: Explainable AI for Raman Spectroscopy.’ In: 9th International Workshop on Health Intelligence (W3PHIAI-25), AAAI. arXiv (Cornell University). https://arxiv.org/pdf/2503.14567

Navaratnarajah, M., Martin, S.A., Kelly, D.A., Blake, N. and Chockler, H. (2025) ‘3D ReX: Causal Explanations in 3D Neuroimaging Classification.’ Poster in: Imageomics-AAAI-25. arXiv (Cornell University). https://arxiv.org/pdf/2502.12181 (without proceedings)

De Sousa Ribeiro, F., Todd, O., Jones, C., Kori, A., Mehta, R. and Glocker, B. (2025) ‘Flow Stochastic Segmentation Networks.’ In: International Conference on Computer Vision (ICCV 2025), 19–23 October, Honolulu, Hawaii. arXiv. https://arxiv.org/abs/2507.18838

Mehta, R., De Sousa Ribeiro, F., Xia, T., Roschewitz, M., Santhirasekaram, A., Marshall, D. C. and Glocker, B. (2025) ‘CF-Seg: Counterfactuals meet Segmentations.’ In: International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2025), 23–27 September, Daejeon, South Korea. arXiv. https://arxiv.org/abs/2506.16213

Roschewitz, M., De Sousa Ribeiro, F., Xia, T., Khara, G. and Glocker, B. (2025) ‘Robust image representations with counterfactual contrastive learning.’ Medical Image Analysis.https://www.sciencedirect.com/science/article/pii/S1361841525002154?via%3Dihub

Oberije, C. J. G., Currie, R., Leaver, A., Redman, A., Teh, W., Sharma, N., Fox, G., Glocker, B., Khara, G., Nash, J., Ng, A. Y. and Kecskemethy, P. D. (2025) ‘Assessing 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.’ BMJ Health & Care Informatics. https://informatics.bmj.com/

Xia, T., Sinclair, M., Schuh, A., De Sousa Ribeiro, F., Mehta, R., Rasal, R., Puyol-Antón, E., Gerber, S., Petersen, K., Schaap, M. and Glocker, B. (2025) ‘Segmentor-guided Counterfactual Fine-Tuning for Locally Coherent and Targeted Image Synthesis.’ In: International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2025), 23–27 September, Daejeon, South Korea. Segmentor-Guided Counterfactual Fine-Tuning for Locally Coherent and Targeted Image Synthesis

Machlanski, D., Riley, S., Moroshko, E., Butler, K., Dimitrakopoulos, P., Melistas, T., Chanchal, A., McDonagh, S., Silva, R., and Tsaftaris, S. A. (2025)‘A shift in perspective on causality in domain generalization.’ UKAIRS Symposium 2025. https://arxiv.org/abs/2508.12798

Jenkins, A., Osorio, E. V., Green, A., van Herk, M., Sperrin, M., and McWilliam, A. (2025) ‘Methods of causal effect estimation for high‐dimensional treatments: A radiotherapy simulation study.’ Medical Physics, 52(7), e17919.https://doi.org/10.1002/mp.17919

Martin, G. P., Pate, A., Bladon, S., Sperrin, M., and Riley, R. D. (2025) ‘A decision-analytical perspective on incorporating multiple outcomes in the production of clinical prediction models: defining a taxonomy of risk estimands.’ BMC Medicine, 23(1), 142. https://doi.org/10.1186/s12916-025-03978-3

Todd, O., Kim, S., Mackay, K., Mehta, R., De Sousa Ribeiro, F., Bernstein, D., Taylor, A., and Glocker, B. (2025) ‘Delineation uncertainty from clinician ranges in cervical cancer radiotherapy planning.’ MICCAI Workshop on Computer-Aided Pelvic Imaging for Female Health (CAPI 2025). Delineation Uncertainty from Clinician Ranges in Cervical Cancer Radiotherapy Planning | Skin Image Analysis, and Computer-Aided Pelvic Imaging for Female Health

Rasal, R., Kori, A., and Glocker, B. (2025) ‘Causal representation learning with observational grouping for CXR classification.’ MICCAI Workshop on Fairness of AI in Medical Imaging (FAIMI 2025).https://arxiv.org/abs/2506.20582

Peng, W., Xia, T., De Sousa Ribeiro, F., Bosschieter, T., Adeli, E., Zhao, Q., Glocker, B., and Pohl, K. M. (2025) ‘Latent causal modeling for 3D brain MRI counterfactuals.’ MICCAI Workshop on Deep Generative Models (DGM4MICCAI 2025). [2409.05585] Latent 3D Brain MRI Counterfactual

De Sousa Ribeiro, F., Santhirasekaram, A., and Glocker, B. (2025). ‘Counterfactual Identifiability via Dynamic Optimal Transport.’ Advances in Neural Information Processing Systems (NeurIPS 2025). https://arxiv.org/abs/2510.08294

Pate, A., Jiang, B., Huang, J. (Yun-Ting), Griffiths, S., Stables, D., McMillan, B., & Sperrin, M. (2025). ‘CHARIOT: Development and Internal Validation of a Cardiovascular Health Assessment and Risk-based Intervention Optimisation Tool.’ medRxiv. https://doi.org/10.1101/2025.09.18.25336064

Roschewitz, M., Mehta, R., De Sousa Ribeiro, F., and Glocker, B. (2025) Where are we with calibration under dataset shift in image classification? Transactions on Machine Learning Research. Available at: hhttps://arxiv.org/abs/2507.07780

Camilleri, M.P.J., Gouzou, D., Al-Wasity, S., Mookiah, M.R.K., Valdes Hernandez, M., Alex, B., Tsaftaris, S.A., Brooks, A., MacLeod, R., Wu, H., Bauer, B., Grover, C., Reel, P., Krueger, S., Tobin, R., Steele, J.D., Mair, G., Wardlaw, J., Doney, A., Trucco, E. and Whiteley, W. (2025) ‘A Large Dataset of Brain Imaging Linked to Health Systems Data: The Curation and Access to a Whole System National Cohort from NHS Scotland.’ medRxiv. Available at: https://www.medrxiv.org/content/10.1101/2025.10.21.25338469v1

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Sanchez, P., Machlanski, D., McDonagh, S. and Tsaftaris, S.A. (2025) ‘Causal ordering for structure learning from time series,’ Transactions on Machine Learning Research. Available at: https://arxiv.org/abs/2510.24639).

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Beckers, S., Chockler, H. and Halpern, J.Y. (2024) 'A causal analysis of harm,' Minds andMachines, 34(3). https://doi.org/10.1007/s11023-024-09689-7

Benitez-Aurioles, J., Joules, A., Brusini, I., Peek, N. and Sperrin, M. (2024) ‘Understanding algorithmic fairness for clinical prediction in terms of subgroup net benefit and health equity.’ arXiv (Cornell University). https://doi:10.48550/arXiv.2412.07879 

Benitez‑Aurioles, J., Wynants, L., Peek, N., Goodley, P., Crosbie, P. and Sperrin, M. (2024) ‘The continuous net benefit: Assessing the clinical utility of prediction models when informing a continuum of decisions.’ arXiv (Cornell University). https://doi:10.48550/arXiv.2412.07882 

Chockler, H. and Halpern, J.Y. (2024) ‘Explaining image classifiers.’ In: 21st International Conference on Principles of Knowledge Representation and Reasoning (KR 2024). pp. 264–272. https://doi:10.24963/kr.2024/25 

Dutt, R., Bohdal, O., Tsaftaris, S.A. and Hospedales, T. (2023) 'FairTune: Optimizing parameter efficient fine tuning for fairness in medical image analysis,' arXiv (Cornell University). https://doi.org/10.48550/arxiv.2310.05055

Gopinath, K., Hoopes, A., Alexander, D.C., Arnold, S.E., Balbastre, Y., Billot, B., Casamitjana, A., Cheng, Y., Zhi Chua, R.Y., Edlow, B.L., Fischl, B., Gazula, H., Hoffmann, M., Dirk Keene, C., Kim, S., Kimberly, W.T., Laguna, S., Larson, K.E., Leemput, K.V., Puonti, O., Rodrigues, L.M., Rosen, M.S., Tregidgo, H.F.J., Varadarajan, D., Young, S.I., Dalca, A.V. and Iglesias, J.E. (2024) 'Synthetic data in generalizable, learning-based neuroimaging,' Imaging Neuroscience, 2, pp. 1–22. https://doi.org/10.1162/imag_a_00337

Gultchin, L., Guo, S., Malek, A., Chiappa, S. and Silva, R. (2024) ‘Pragmatic Fairness: Developing Policies with Outcome Disparity Control.’ In: Proceedings of the Third Conference on Causal Learning and Reasoning, 236, pp. 243–264. https://doi:10.48550/arXiv.2301.12278

Jones, C., Castro, D.C., De Sousa Ribeiro, F., Oktay, O., McCradden, M. and Glocker, B. (2024) 'A causal perspective on dataset bias in machine learning for medical imaging,' Nature Machine Intelligence, 6(2), pp. 138–146. https://doi.org/10.1038/s42256-024-00797-8

Kori, A., Locatello, F., Santhirasekaram, A., Toni, F., Glocker, B. and De Sousa Ribeiro, F. (2024) ‘Identifiable object‑centric representation learning via probabilistic slot attention.’ In: Thirty‑Eighth Annual Conference on Neural Information Processing Systems (NeurIPS 2024). arXiv:2406.07141. https://doi:10.48550/arXiv.2406.07141

Lin, L., Poppe, K., Wood, A., Martin, G.P., Peek, N., Sperrin, M. (2024) 'Making predictions under interventions: a case study from the PREDICT-CVD cohort in New Zealand primary care,' Frontiers in Epidemiology, 4. https://doi.org/10.3389/fepid.2024.1326306

Li, K., Xie, W., Huang, Y., Deng, D., Hong, L., Li, Z., Silva, R. and Zhang, N.L. (2024) 'Dual risk minimization: towards Next-Level robustness in fine-tuning Zero-Shot models,' arXiv (Cornell University). https://doi.org/10.48550/arxiv.2411.19757

Melistas, T., Spyrou, N., Gkouti, N., Sanchez, P., Vlontzos, A., Panagakis, Y., Papanastasiou, G. and Tsaftaris, S.A. (2024) ‘Benchmarking counterfactual image generation.’ arXiv (Cornell University). https://doi:10.48550/arXiv.2403.20287

Peng, W., Xia, T., De Sousa Ribeiro, F., Bosschieter, T., Adeli, E., Zhao, Q., Glocker, B. and Pohl, K.M. (2024) ‘Latent 3D Brain MRI Counterfactual.’ arXiv (Cornell University). https://doi:10.48550/arXiv.2409.05585 

Penn, J., Gunderson, L.M., Bravo‑Hermsdorff, G., Silva, R. and Watson, D.S. (2024) ‘BudgetIV: Optimal partial identification of causal effects with mostly invalid instruments.’ arXiv (Cornell University). https://doi:10.48550/arXiv.2411.06913

Ploddi, K., Sperrin, M., Martin, G.P. and O’Connell, M.M. (2024) ‘Scoping review of methodology for aiding generalisability and transportability of clinical prediction models.’ arXiv (Cornell University). https://doi:10.48550/arXiv.2412.04275 

Pranger, S., Chockler, H., Tappler, M. and Könighofer, B. (2024) ‘Test Where Decisions Matter: Importance‑driven Testing for Deep Reinforcement Learning,’ In: Thirty‑Eighth Annual Conference on Neural Information Processing Systems (NeurIPS 2024). arXiv:2411.07700. https://doi:10.48550/arXiv.2411.07700

Roschewitz, M., De Sousa Ribeiro, F., Xia, T., Khara, G. and Glocker, B. (2024) 'Counterfactual contrastive learning: robust representations via causal  image synthesis,' arXiv (Cornell University). https://doi.org/10.48550/arxiv.2403.09605

van Geloven, N., Keogh, R.H., van Amsterdam, W., Cinà, G., Krijthe, J.H., Peek, N., Luijken, K., Magliacane, S., Morzywołek, P., van Ommen, T., Putter, H., Sperrin, M., Wang, J., Weir, D.L. and Didelez, V. (2024) ‘The risks of risk assessment: causal blind spots when using prediction models for treatment decisions.’arXiv (Cornell University). https://doi:10.48550/arXiv.2402.17366

Watson, D.S., Penn, J., Gunderson, L.M., Bravo-Hermsdorff, G., Mastouri, A. and Silva, R. (2024) 'Bounding Causal Effects with Leaky Instruments,' arXiv (Cornell University). https://doi.org/10.48550/arxiv.2404.04446

Xia, T., Roschewitz, M., De Sousa Ribeiro, F., Jones, C. and Glocker, B. (2024) 'Mitigating attribute amplification in counterfactual image generation,' in Lecture notes in computer science, pp. 546–556. https://doi.org/10.1007/978-3-031-72117-5_51

Yu, J., Koukorinis, A., Colombo, N., Zhu, Y. and Silva, R. (2024) 'Structured learning of compositional sequential interventions,' arXiv (Cornell University). https://doi.org/10.48550/arxiv.2406.05745

Yu, J., Zhou, Y., He, Y., Zhang, N.L. and Silva, R. (2024) ‘Fine‑Tuning Pre‑trained Language Models for Robust Causal Representation Learning.’ arXiv (Cornell University). https://doi:10.48550/arXiv.2410.14375