Our Publications

While gradually building up a strong tradition in publishing, our research teams have developed an internal publication strategy where they work either individually or collaboratively on publications linked to their current projects.

To assist with their research, they have established strong links with local universities, including Glasgow, Edinburgh, Heriot Watt, Aberdeen and St Andrews, and work with both clinical and academic collaborators to maintain a long term strategy to deliver new and exciting projects. To further support their research our scientists have opportunities to attend international conferences focused on medical imaging, deep learning and artificial intelligence.

Browse our publications

2025

Automated Neuroradiological Support Systems for Multiple Cerebrovascular Disease Markers: A Systematic Review and Meta-Analysis

Computer methods and programs in biomedicine, p.108715., 2025

Phitidis, J., O’Neil, A.Q., Whiteley, W.N., Alex, B., Wardlaw, J.M., Bernabeu, M.O. and Hernández, M.V.

Evaluating Multiomics Integration Architectures for Training with Structured Missingness

bioRxiv 2025.09.10.672554; doi: https://doi.org/10.1101/2025.09.10.672554, 2025

Simon Fisher, Jacob Bradley, George Lansdown, Owen Anderson, Russell Hung, James Lesh, Murray Cutforth, Ian Poole, Jeremy P. Voisey

Conformal Coronary Calcification Volume Estimation with Conditional Coverage via Histogram Clustering

2025 IEEE International Symposium on Biomedical Imaging (ISBI), 2025

Jaubert, O, Mohammadi, S Goatman, K.A, Mikhael, S, Bradley, C, Hughes, R, Good, R, Hipwell, J, Dahdouh, S.

The Colour of Time: Detecting Glioma IDH Mutation Status in MRI Through Pseudo-Coloured Transfer Learning

Medical Image Understanding and Analysis (MIUA 2025), 2025

MacKinnon, H., Paracchini, S., Harris-Birtill, D., Hipwell, J., Goatman, K.

Evaluation of Large Language Models with Clinical Guidance for Vetting Outpatient MRI Lumbar Spine Referrals

Global Conference of Artificial Intelligence of the Royal College of Radiologists, 2025

Clackett, W., Alsusa, H., Watson, H., Kascenas, A., Scott, D., Kanodia, A.K., Oliver, T.B. and O'Neil, A.Q.

2024

Assessing Machine Learning for Diagnostic Classification of Hypertension Types Identified by Ambulatory Blood Pressure Monitoring

CJC open, 6(6), pp.798-804., 2024

Tran, T.Q.B., Lip, S., du Toit, C., Kalaria, T.K., Bhaskar, R.K., O’Neil, A.Q., Graff, B., Hoffmann, M., Szyndler, A., Polonis, K. and Wolf, J.

Group Distributionally Robust Knowledge Distillation

Machine Learning in Medical Imaging. MLMI 2023. Lecture Notes in Computer Science, vol 14349. Springer, Cham., 2024

Vilouras, K., Liu, X., Sanchez, P., O'Neil, A.Q., Tsaftaris, S.A.

Artificial Intelligence Software to Expedite Electronic Health Record Review in Acute Stroke

10th European Stroke Organisation Conference (ESOC), 2024

C Brown, S Denham, A Meikle, S Mikhael3, H Watson, A Wellington, M Cutforth, K Goatman, A.Q. O’Neil, A Weir, K.W. Muir.

Confounder-Aware Image Synthesis for Pathology Segmentation in New Magnetic Resonance Imaging Sequences

Annual Conference on Medical Image Understanding and Analysis (pp. 227-241). Cham: Springer Nature Switzerland., 2024

Phitidis, J., Kascenas, A., Hernández, M.V., Whiteley, W.N., Wardlaw, J.M. and O’Neil, A.Q.,

All You Need is a Guiding Hand: Mitigating Shortcut Bias in Deep Learning Models for Medical Imaging.

In: Puyol-Antón, E., et al. Ethics and Fairness in Medical Imaging. FAIMI EPIMI 2024 2024. Lecture Notes in Computer Science, vol 15198. Springer, Cham. https://doi.org/10.1007/978-3-031-72787-0_7, 2024

Boland, C.T, Goatman, K.A, Hipwell, J., Tsaftaris, S.A., Dahdouh, S.

There Are No Shortcuts To Anywhere Worth Going: Identifying Shortcuts in Deep Learning Models for Medical Image Analysis.

Medical Imaging with Deep Learning (MIDL), 2024, 2024

Boland, C.T, Goatman, K.A, Tsaftaris, S.A., Dahdouh, S.

TIST-Net: Style Transfer in Dynamic Contrast Enhanced MRI Using Spatial and Temporal Information

Phys Med Biol. 2024 May 27;69(11). doi: 10.1088/1361-6560/ad4193. PMID: 38648788., 2024

Tattersall, A.G, Goatman, K.A, Kershaw, L.E., Semple, S.I.K, Dahdouh, S.

Clinically Focussed Evaluation of Anomaly Detection and Localisation Methods Using Inpatient CT Head Data

In: Xue, Y., Chen, C., Chen, C., Zuo, L., Liu, Y. (eds) Data Augmentation, Labelling, and Imperfections. MICCAI 2023, 2024

Kascenas A, Wang C, Schrempf P, Grech R, Goh HL, Hall M, O’Neil AQ.

2023

Acute stroke CDS: automatic retrieval of thrombolysis contraindications from unstructured clinical letters

Frontiers in Digital Health, 5., 2023

Cutforth, M., Watson, H., Brown, C., Wang, C., Thomson, S., Fell, D., Dilys, V., Scrimgeour, M., Schrempf, P., Lesh, J., Muir, K., Weir, A., & O’Neil, A. Q. (2023).

Towards Tumour Graph Learning for Survival Prediction in Head & Neck Cancer Patients.

In: Andrearczyk, V., Oreiller, V., Hatt, M., Depeursinge, A. (eds) Head and Neck Tumor Segmentation and Outcome Prediction. HECKTOR 2022. Lecture Notes in Computer Science, vol 13626., 2023

Juanco-Müller, Á.V., Mota, J.F.C., Goatman, K., Hoogendoorn, C. (2023).

Compositional Representation Learning for Brain Tumour Segmentation. In: Koch, L., et al. Domain Adaptation and Representation Transfer

Compositional Representation Learning for Brain Tumour Segmentation. In: Koch, L., et al. Domain Adaptation and Representation Transfer. DART 2023. Lecture Notes in Computer Science, vol 14293. Springer, Cham. , 2023

Liu, X., Kascenas, A., Watson, H., Tsaftaris, S.A., O’Neil, A.Q.

SIC-InfoGAN: Learning Unsupervised Disentangled Representations by Maximising Approximated Mutual Information

HSIC-InfoGAN: Learning Unsupervised Disentangled Representations by Maximising Approximated Mutual Information. In Medical Applications with Disentanglements: First MICCAI Workshop (MAD) (pp. 15-21). Springer, Cham., 2023

Liu, X., Thermos, S., Sanchez, P., O’Neil, A.Q. and Tsaftaris, S.A.

Diffusion Models for Causal Discovery via Topological Ordering

Diffusion Models for Causal Discovery via Topological Ordering. In The Eleventh International Conference on Learning Representations (ICLR)., 2023

Sanchez, P., Liu, X., O'Neil, A.Q. and Tsaftaris, S.A.

Understanding the performance and reliability of NLP tools: a comparison of four NLP tools predicting stroke phenotypes in radiology reports

Front. Digit. Health 5:1184919 https://doi.org/10.3389/fdgth.2023.1184919, 2023

Casey A, Davidson E, Grover C, Tobin R, Grivas A, Zhang H, Schrempf P, O'Neil AQ, Lee L, Walsh M, Pellie F, Ferguson K, Cvoro V, Wu H, Whalley H, Mair G, Whiteley W and Alex B

Finding-Aware Anatomical Tokens for Chest X-Ray Automated Reporting

In: Cao, X., Xu, X., Rekik, I., Cui, Z., Ouyang, X. (eds) Machine Learning in Medical Imaging. MLMI 2023. Lecture Notes in Computer Science, vol 14348. Springer, Cham., 2023

Dalla Serra, F., Wang, C., Deligianni, F., Dalton, J., O'Neil, A.Q. (2024).

The Topology-Overlap Trade-Off in Retinal Arteriole-Venule Segmentation

Medical Imaging 2023: Image Processing (Vol. 12464, pp. 576-586). SPIE., 2023

Juanco-Muller, A. V., Mota, J. F., Goatman, K. A., & Hoogendoorn, C..

Towards Tumour Graph Learning for Survival Prediction in Head & Neck Cancer Patients

Towards Tumour Graph Learning for Survival Prediction in Head & Neck Cancer Patients. In: Andrearczyk, V., Oreiller, V., Hatt, M., Depeursinge, A. (eds) Head and Neck Tumor Segmentation and Outcome Prediction. HECKTOR 2022. Lecture Notes in Computer Science, vol 13626., 2023

Juanco-Muller, A.V., Mota, J.F.C., Goatman, K., Hoogendoorn, C..

The Role of Noise in Denoising Models for Anomaly Detection in Medical Images

The role of noise in denoising models for anomaly detection in medical images. Medical Image Analysis, p.102963., 2023

Kascenas, A., Sanchez, P., Schrempf, P., Wang, C., Clackett, W., Mikhael, S.S., Voisey, J.P., Goatman, K., Weir, A., Pugeault, N., Tsaftaris, S.A. and O'Neil, A.Q..

Compositional Representation Learning for Brain Tumour Segmentation

Liu, X., Kascenas, A., Watson, H., Tsaftaris, S.A., O'Neil, A.Q. (2024).

HSIC-InfoGAN: Learning Unsupervised Disentangled Representations by Maximising Approximated Mutual Information

Liu, X., Thermos, S., Sanchez, P., O'Neil, A.Q. and Tsaftaris, S.A..

Diffusion Models for Causal Discovery via Topological Ordering

Diffusion Models for Causal Discovery via Topological Ordering. In The Eleventh International Conference on Learning Representations (ICLR)., 2023

Sanchez, P., Liu, X., O'Neil, A.Q. and Tsaftaris, S.A., 2023.

Can a Single Image Processing Algorithm Work Equally Well Accross all phases of DCE-MRI?

Proc. SPIE 12464, Medical Imaging 2023: Image Processing, 124642X, 2023

Tattersall, A.G, Goatman, K.A, Kershaw, L.E., Semple, S.I.K, Dahdouh, S.

Improving Style Transfer in Dynamic Contrast Enhanced MRI Using a Spatio-Temporal Approach

MICCAI: International Workshop on Simulation and Synthesis in Medical Imaging, 2023

Tattersall, A.G, Goatman, K.A, Kershaw, L.E., Semple, S.I.K, Dahdouh, S.

Our Publications

Browse our publications

2025

Automated Neuroradiological Support Systems for Multiple Cerebrovascular Disease Markers: A Systematic Review and Meta-Analysis

Evaluating Multiomics Integration Architectures for Training with Structured Missingness

Conformal Coronary Calcification Volume Estimation with Conditional Coverage via Histogram Clustering

The Colour of Time: Detecting Glioma IDH Mutation Status in MRI Through Pseudo-Coloured Transfer Learning

Evaluation of Large Language Models with Clinical Guidance for Vetting Outpatient MRI Lumbar Spine Referrals

2024

Assessing Machine Learning for Diagnostic Classification of Hypertension Types Identified by Ambulatory Blood Pressure Monitoring

Group Distributionally Robust Knowledge Distillation

Artificial Intelligence Software to Expedite Electronic Health Record Review in Acute Stroke

Confounder-Aware Image Synthesis for Pathology Segmentation in New Magnetic Resonance Imaging Sequences

All You Need is a Guiding Hand: Mitigating Shortcut Bias in Deep Learning Models for Medical Imaging.

There Are No Shortcuts To Anywhere Worth Going: Identifying Shortcuts in Deep Learning Models for Medical Image Analysis.

TIST-Net: Style Transfer in Dynamic Contrast Enhanced MRI Using Spatial and Temporal Information

Clinically Focussed Evaluation of Anomaly Detection and Localisation Methods Using Inpatient CT Head Data

2023

Acute stroke CDS: automatic retrieval of thrombolysis contraindications from unstructured clinical letters

Towards Tumour Graph Learning for Survival Prediction in Head & Neck Cancer Patients.

Compositional Representation Learning for Brain Tumour Segmentation. In: Koch, L., et al. Domain Adaptation and Representation Transfer

SIC-InfoGAN: Learning Unsupervised Disentangled Representations by Maximising Approximated Mutual Information

Diffusion Models for Causal Discovery via Topological Ordering

Understanding the performance and reliability of NLP tools: a comparison of four NLP tools predicting stroke phenotypes in radiology reports

Finding-Aware Anatomical Tokens for Chest X-Ray Automated Reporting

The Topology-Overlap Trade-Off in Retinal Arteriole-Venule Segmentation

Towards Tumour Graph Learning for Survival Prediction in Head & Neck Cancer Patients

The Role of Noise in Denoising Models for Anomaly Detection in Medical Images

Compositional Representation Learning for Brain Tumour Segmentation

HSIC-InfoGAN: Learning Unsupervised Disentangled Representations by Maximising Approximated Mutual Information

Diffusion Models for Causal Discovery via Topological Ordering

Can a Single Image Processing Algorithm Work Equally Well Accross all phases of DCE-MRI?

Improving Style Transfer in Dynamic Contrast Enhanced MRI Using a Spatio-Temporal Approach

2022

Survey: Leakage and privacy at inference time. IEEE Transactions on Pattern Analysis and Machine Intelligence

Multimodal Generation of Radiology Reports using Knowledge-Grounded Extraction of Entities and Relations

Improving Image Representations via MoCo Pre-training for Multimodal CXR Classification

Investigating the transcriptome of adenomatous colorectal polyps in the context of metachronous recurrence

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