Outputs (10)

Racial Bias within Face Recognition: A Survey (2024)
Journal Article
Yucer, S., Tekras, F., Al Moubayed, N., & Breckon, T. P. (2025). Racial Bias within Face Recognition: A Survey. ACM Computing Surveys, 57(4), 1-39. https://doi.org/10.1145/3705295

Facial recognition is one of the most academically studied and industrially developed areas within computer vision where we readily find associated applications deployed globally. This widespread adoption has uncovered significant performance variati... Read More about Racial Bias within Face Recognition: A Survey.

Performance of machine learning versus the national early warning score for predicting patient deterioration risk: a single-site study of emergency admissions (2024)
Journal Article
Watson, M., Boulitsakis Logothetis, S., Green, D., Holland, M., Chambers, P., & Al Moubayed, N. (2024). Performance of machine learning versus the national early warning score for predicting patient deterioration risk: a single-site study of emergency admissions. BMJ Health & Care Informatics, 31(1), Article e101088. https://doi.org/10.1136/bmjhci-2024-101088

Objectives: Increasing operational pressures on emergency departments (ED) make it imperative to quickly and accurately identify patients requiring urgent clinical intervention. The widespread adoption of electronic health records (EHR) makes rich fe... Read More about Performance of machine learning versus the national early warning score for predicting patient deterioration risk: a single-site study of emergency admissions.

Premature mortality analysis of 52,000 deceased cats and dogs exposes socioeconomic disparities (2024)
Journal Article
Farrell, S., Anderson, K., Noble, P.-J. M., & Al Moubayed, N. (2024). Premature mortality analysis of 52,000 deceased cats and dogs exposes socioeconomic disparities. Scientific Reports, 14(1), Article 28763. https://doi.org/10.1038/s41598-024-77385-8

Monitoring mortality rates offers crucial insights into public health by uncovering the hidden impacts of diseases, identifying emerging trends, optimising resource allocation, and informing effective policy decisions. Here, we present a novel approa... Read More about Premature mortality analysis of 52,000 deceased cats and dogs exposes socioeconomic disparities.

From prediction to practice: mitigating bias and data shift in machine-learning models for chemotherapy-induced organ dysfunction across unseen cancers (2024)
Journal Article
Watson, M., Chambers, P., Steventon, L., Harmsworth King, J., Ercia, A., Shaw, H., & Al Moubayed, N. (2024). From prediction to practice: mitigating bias and data shift in machine-learning models for chemotherapy-induced organ dysfunction across unseen cancers. BMJ Oncology, 3(1), Article e000430. https://doi.org/10.1136/bmjonc-2024-000430

Objectives: Routine monitoring of renal and hepatic function during chemotherapy ensures that treatment-related organ damage has not occurred and clearance of subsequent treatment is not hindered; however, frequency and timing are not optimal. Model... Read More about From prediction to practice: mitigating bias and data shift in machine-learning models for chemotherapy-induced organ dysfunction across unseen cancers.

SciMMIR: Benchmarking Scientific Multi-modal Information Retrieval (2024)
Presentation / Conference Contribution
Wu, S., Li, Y., Zhu, K., Zhang, G., Liang, Y., Ma, K., Xiao, C., Zhang, H., Yang, B., Chen, W., Huang, W., Al Moubayed, N., Fu, J., & Lin, C. (2024, August). SciMMIR: Benchmarking Scientific Multi-modal Information Retrieval. Presented at ACL 2024: Annual Meeting of the Association for Computational Linguistics, Bangkok, Thailand

Multi-modal information retrieval (MMIR) is a rapidly evolving field where significant progress has been made through advanced representation learning and cross-modality alignment research, particularly in image-text pairs. However, current benchmark... Read More about SciMMIR: Benchmarking Scientific Multi-modal Information Retrieval.

Text mining for disease surveillance in veterinary clinical data: part two, training computers to identify features in clinical text (2024)
Journal Article
Davies, H., Nenadic, G., Alfattni, G., Arguello Casteleiro, M., Al Moubayed, N., Farrell, S., Radford, A. D., & Noble, P.-J. M. (2024). Text mining for disease surveillance in veterinary clinical data: part two, training computers to identify features in clinical text. Frontiers in Veterinary Science, 11, Article 1352726. https://doi.org/10.3389/fvets.2024.1352726

In part two of this mini-series, we evaluate the range of machine-learning tools now available for application to veterinary clinical text-mining. These tools will be vital to automate extraction of information from large datasets of veterinary clini... Read More about Text mining for disease surveillance in veterinary clinical data: part two, training computers to identify features in clinical text.

Disentangling Racial Phenotypes: Fine-Grained Control of Race-related Facial Phenotype Characteristics (2024)
Presentation / Conference Contribution
Yucer, S., Abarghouei, A. A., Al Moubayed, N., & Breckon, T. P. (2024, June). Disentangling Racial Phenotypes: Fine-Grained Control of Race-related Facial Phenotype Characteristics. Presented at 2024 International Joint Conference on Neural Networks (IJCNN), Yokohama, Japan

Achieving an effective fine-grained appearance variation over 2D facial images, whilst preserving facial identity, is a challenging task due to the high complexity and entanglement of common 2D facial feature encoding spaces. Despite these challenges... Read More about Disentangling Racial Phenotypes: Fine-Grained Control of Race-related Facial Phenotype Characteristics.

Explainable text-tabular models for predicting mortality risk in companion animals (2024)
Journal Article
Burton, J., Farrell, S., Mäntylä Noble, P.-J., & Al Moubayed, N. (2024). Explainable text-tabular models for predicting mortality risk in companion animals. Scientific Reports, 14(1), Article 14217. https://doi.org/10.1038/s41598-024-64551-1

As interest in using machine learning models to support clinical decision-making increases, explainability is an unequivocal priority for clinicians, researchers and regulators to comprehend and trust their results. With many clinical datasets contai... Read More about Explainable text-tabular models for predicting mortality risk in companion animals.

CXR-IRGen: An Integrated Vision and Language Model for the Generation of Clinically Accurate Chest X-Ray Image-Report Pairs (2024)
Presentation / Conference Contribution
Shentu, J., & Al Moubayed, N. (2024, January). CXR-IRGen: An Integrated Vision and Language Model for the Generation of Clinically Accurate Chest X-Ray Image-Report Pairs. Presented at 2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), Waikoloa, Hawaii, USA

Chest X-Ray (CXR) images play a crucial role in clinical practice, providing vital support for diagnosis and treatment. Augmenting the CXR dataset with synthetically generated CXR images annotated with radiology reports can enhance the performance of... Read More about CXR-IRGen: An Integrated Vision and Language Model for the Generation of Clinically Accurate Chest X-Ray Image-Report Pairs.

Text mining for disease surveillance in veterinary clinical data: part one, the language of veterinary clinical records and searching for words (2024)
Journal Article
Davies, H., Nenadic, G., Alfattni, G., Arguello Casteleiro, M., Al Moubayed, N., Farrell, S. O., …Noble, P. M. (2024). Text mining for disease surveillance in veterinary clinical data: part one, the language of veterinary clinical records and searching for words. Frontiers in Veterinary Science, 11, Article 1352239. https://doi.org/10.3389/fvets.2024.1352239

The development of natural language processing techniques for deriving useful information from unstructured clinical narratives is a fast-paced and rapidly evolving area of machine learning research. Large volumes of veterinary clinical narratives no... Read More about Text mining for disease surveillance in veterinary clinical data: part one, the language of veterinary clinical records and searching for words.