Pediatrics in artificial intelligence era: A systematic review on challenges, opportunities, and explainability

Article

Balla, Y., Tirunagari, S. and Windridge, D. 2023. Pediatrics in artificial intelligence era: A systematic review on challenges, opportunities, and explainability. Indian Pediatrics. 60 (7), pp. 561-569. https://doi.org/10.1007/s13312-023-2936-8
TypeArticle
TitlePediatrics in artificial intelligence era: A systematic review on challenges, opportunities, and explainability
AuthorsBalla, Y., Tirunagari, S. and Windridge, D.
Abstract

Background: The emergence of artificial intelligence (AI) tools such as ChatGPT and Bard is disrupting a broad swathe of fields, including medicine. In pediatric medicine, AI is also increasingly being used across multiple subspecialties. However, the practical application of AI still faces a number of key challenges. Consequently, there is a requirement for a concise overview of the roles of AI across the multiple domains of pediatric medicine, which the current study seeks to address.

Aim: To systematically assess the challenges, opportunities, and explainability of AI in pediatric medicine.

Methodology: A systematic search was carried out on peer-reviewed databases, PubMed Central, Europe PubMed Central, and grey literature using search terms related to machine learning (ML) and AI for the years 2016 to 2022 in the English language. A total of 210 articles were retrieved that were screened with PRISMA for abstract, year, language, context, and proximal relevance to research aims. A thematic analysis was carried out to extract findings from the included studies.

Results: Twenty articles were selected for data abstraction and analysis, with three consistent themes emerging from these articles. In particular, eleven articles address the current state-of-the-art application of AI in diagnosing and predicting health conditions such as behavioral and mental health, cancer, syndromic and metabolic diseases. Five articles highlight the specific challenges of AI deployment in pediatric medicines: data security, handling, authentication, and validation. Four articles set out future opportunities for AI to be adapted: the incorporation of Big Data, cloud computing, precision medicine, and clinical decision support systems. These studies collectively critically evaluate the potential of AI in overcoming current barriers to adoption.

Conclusion: AI is proving disruptive within pediatric medicine and is presently associated with challenges, opportunities, and the need for explainability. AI should be viewed as a tool to enhance and support clinical decision-making rather than a substitute for human judgement and expertise. Future research should consequently focus on obtaining comprehensive data to ensure the generalizability of research findings.

KeywordsArtificial intelligence; Data science; Deep learning; Large language model; Neoplasms; Clinical Decision-Making; Child; Pediatrics; Humans
Sustainable Development Goals3 Good health and well-being
Middlesex University ThemeHealth & Wellbeing
PublisherIndian Academy of Pediatrics
Springer
JournalIndian Pediatrics
ISSN0019-6061
Electronic0974-7559
Publication dates
Online24 Jul 2023
Print15 Jul 2023
Publication process dates
Deposited01 Jun 2023
Accepted14 May 2023
Output statusPublished
Publisher's version
License
File Access Level
Open
Accepted author manuscript
File Access Level
Open
Copyright Statement

This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Additional information

S097475591600533

Digital Object Identifier (DOI)https://doi.org/10.1007/s13312-023-2936-8
PubMed ID37424120
Web of Science identifierWOS:001034797100014
LanguageEnglish
Permalink -

https://repository.mdx.ac.uk/item/8q64q

Log in to edit

Download files

Publisher's version
s13312-023-2936-8.pdf
License: CC BY 4.0
File access level: Open

Accepted author manuscript
Paediatric_AI_Paper (6).pdf
File access level: Open

  • 105
    total views
  • 18
    total downloads
  • 2
    views this month
  • 1
    downloads this month

Export as

Related outputs

Quantum enhanced knowledge distillation
Simone, P., Lavagna, L., De Falco, F,, Ceschini, A., Rosato, A., Windridge, D. and Panella, M. 2024. Quantum enhanced knowledge distillation. Quantum Techniques in Machine Learning 2024 Conference. Melbourne, Australia 24 - 29 Nov 2024
An evolutionary approach to automated class-specific data augmentation for image classification
Marc, S., Belavkin, R., Windridge, D. and Gao, X. 2024. An evolutionary approach to automated class-specific data augmentation for image classification. Moosaei, H., Hladík, M. and Pardalos, P. (ed.) 6th International Conference on the Dynamics of Information Systems. Prague, Czech Republic 03 - 06 Dec 2023 Springer. pp. 170–185 https://doi.org/10.1007/978-3-031-50320-7_12
Faithful Counterfactual Visual Explanations (FCVE)
Khan, B., Tariq, S., Zia, T., Ahsan, M. and Windridge, D. 2024. Faithful Counterfactual Visual Explanations (FCVE). Knowledge-Based Systems. 294. https://doi.org/10.1016/j.knosys.2024.111668
A convex selective segmentation model based on a piece-wise constant metric guided edge detector function
Khan, M., Ali, H., Zakarya, M., Tirunagari, S., Khan, A., Khan, R., Ahmed, A. and Rada, L. 2022. A convex selective segmentation model based on a piece-wise constant metric guided edge detector function. Research Square. https://doi.org/10.21203/rs.3.rs-2391118/v1
Resource saving via ensemble techniques for quantum neural networks
Incudini, M., Grossi, M., Ceschini, A., Mandarino, A., Panella, M., Vallecorsa, S. and Windridge, D. 2023. Resource saving via ensemble techniques for quantum neural networks. Quantum Machine Intelligence. 5 (2). https://doi.org/10.1007/s42484-023-00126-z
A topological features based quantum kernel
Incudini, M., Martini, F., Di Pierro, A. and Windridge, D. 2023. A topological features based quantum kernel. 7th International Conference on Quantum Techniques in Machine Learning. CERN, Geneva 19 - 24 Nov 2023
Interpretable chronic kidney disease risk prediction from clinical data using machine learning
Chennareddy, V., Tirunagari, S., Mohan, S., Windridge, D. and Balla, Y. 2023. Interpretable chronic kidney disease risk prediction from clinical data using machine learning. 16th Multi-Disciplinary International Conference on Artificial Intelligence (MIWAI 2023). Hyderabad, India 21 2023 - 22 Jul 2024 Springer. https://doi.org/10.1007/978-3-031-36402-0_63
Addressing challenges in healthcare big data analytics
Tirunagari, S., Mohan, S., Windridge, D. and Balla, Y. 2023. Addressing challenges in healthcare big data analytics. 16th Multi-Disciplinary International Conference on Artificial Intelligence (MIWAI 2023). Hyderabad, India 21 2023 - 22 Jul 2024 Springer. https://doi.org/10.1007/978-3-031-36402-0_70
Visual attribution using Adversarial Latent Transformations
Zia, T., Wahab, A., Windridge, D., Tirunagari, S. and Bhatti, N. 2023. Visual attribution using Adversarial Latent Transformations. Computers in Biology and Medicine. 166. https://doi.org/10.1016/j.compbiomed.2023.107521
Deep combination of radar with optical data for gesture recognition: role of attention in fusion architectures
Towakel, P., Windridge, D. and Nguyen, H. 2023. Deep combination of radar with optical data for gesture recognition: role of attention in fusion architectures. IEEE Transactions on Instrumentation and Measurement. 72, pp. 1-15. https://doi.org/10.1109/TIM.2023.3307768
The quantum path kernel: A generalized neural tangent kernel for deep quantum machine learning
Incudini, M., Grossi, M., Mandarino, A., Vallecorsa, S., Di Pierro, A. and Windridge, D. 2023. The quantum path kernel: A generalized neural tangent kernel for deep quantum machine learning. IEEE Transactions on Quantum Engineering. 4. https://doi.org/10.1109/TQE.2023.3287736
Resource saving via ensemble techniques for quantum neural networks
Incudini, M., Grossi, M., Ceschini, A., Mandarino, A., Panella, M., Vallecorsa, S. and Windridge, D. 2023. Resource saving via ensemble techniques for quantum neural networks. https://doi.org/10.48550/arXiv.2303.11283
Reducing the dependency of having prior domain knowledge for effective online information retrieval
Zammit, O., Smith, S., Windridge, D. and De Raffaele, C. 2023. Reducing the dependency of having prior domain knowledge for effective online information retrieval. Expert Systems. 40 (4). https://doi.org/10.1111/exsy.13014
A user-guided personalization methodology to facilitate new smart home occupancy
Ali, S.M.M., Augusto, J., Windridge, D. and Ward, E. 2023. A user-guided personalization methodology to facilitate new smart home occupancy. Universal Access in the Information Society. 22 (3), pp. 869-891. https://doi.org/10.1007/s10209-022-00883-x
A novel kernel based approach to arbitrary length symbolic data with application to type 2 diabetes risk
Nwegbu, N., Tirunagari, S. and Windridge, D. 2022. A novel kernel based approach to arbitrary length symbolic data with application to type 2 diabetes risk. Scientific Reports. 12 (1), pp. 1-16. https://doi.org/10.1038/s41598-022-08757-1
VANT-GAN: adversarial learning for discrepancy-based visual attribution in medical imaging
Zia, T., Murtaza, S., Bhatti, N., Windridge, D. and Nisar, Z. 2022. VANT-GAN: adversarial learning for discrepancy-based visual attribution in medical imaging. Pattern Recognition Letters. 156, pp. 112-118. https://doi.org/10.1016/j.patrec.2022.02.005
The quantum path kernel: A generalized quantum neural tangent kernel for deep quantum machine learning
Incudini, M., Grossi, M., Mandarino, A., Vallecorsa, S., Di Pierro, A. and Windridge, D. 2022. The quantum path kernel: A generalized quantum neural tangent kernel for deep quantum machine learning. https://doi.org/10.48550/arXiv.2212.11826
Discriminator-based adversarial networks for knowledge graph completion
Tubaishat, A., Zia, T., Faiz, R., Al Obediat, F., Shah, B. and Windridge, D. 2022. Discriminator-based adversarial networks for knowledge graph completion. Neural Computing and Applications. https://doi.org/10.1007/s00521-022-07680-w
A generative adversarial network for single and multi-hop distributional knowledge base completion
Zia, T. and Windridge, D. 2021. A generative adversarial network for single and multi-hop distributional knowledge base completion. Neurocomputing. 461, pp. 543-551. https://doi.org/10.1016/j.neucom.2021.04.128
Multi-view convolutional recurrent neural networks for lung cancer nodule identification
Naeem Abid, M., Zia, T., Ghafoor, M. and Windridge, D. 2021. Multi-view convolutional recurrent neural networks for lung cancer nodule identification. Neurocomputing. 453, pp. 299-311. https://doi.org/10.1016/j.neucom.2020.06.144
Generative Adversarial Networks (GANs) in networking: A comprehensive survey & evaluation
Navidan, H., Moshiri, P., Nabati, M., Shahbazian, R., Ghorashi, S., Shah-Mansouri, V. and Windridge, D. 2021. Generative Adversarial Networks (GANs) in networking: A comprehensive survey & evaluation. Computer Networks. 194, pp. 1-21. https://doi.org/10.1016/j.comnet.2021.108149
A genetic deep learning model for electrophysiological soft robotics
Pandey, H. and Windridge, D. 2021. A genetic deep learning model for electrophysiological soft robotics. Balas, V., Jain, L., Balas, M. and Shahbazova, S. (ed.) 8th International Workshop on Soft Computing Applications. Arad, Romania 13 - 15 Sep 2018 Cham Springer. https://doi.org/10.1007/978-3-030-51992-6_12
Exposing students to new terminologies while collecting browsing search data (best technical paper)
Zammit, O., Smith, S., Windridge, D. and De Raffaele, C. 2020. Exposing students to new terminologies while collecting browsing search data (best technical paper). 40th SGAI International Conference on Artificial Intelligence, AI 2020. Cambridge, UK 15 - 17 Dec 2020 Cham Springer. https://doi.org/10.1007/978-3-030-63799-6_1
On the utility of dreaming: a general model for how learning in artificial agents can benefit from data hallucination
Windridge, D., Svensson, H. and Thill, S. 2021. On the utility of dreaming: a general model for how learning in artificial agents can benefit from data hallucination. Adaptive Behavior. 29 (3), pp. 267-280. https://doi.org/10.1177/1059712319896489
An approach to human-machine teaming in legal investigations using anchored narrative visualisation and machine learning
Attfield, S., Fields, B., Windridge, D. and Xu, K. 2019. An approach to human-machine teaming in legal investigations using anchored narrative visualisation and machine learning. Conrad, J., Pickens, J., Jones, A., Baron, J. and Henseler, H. (ed.) First International Workshop on AI and Intelligent Assistance for Legal Professionals in the Digital Workplace (LegalAIIA 2019).. Montreal, Canada 17 Jun 2019 CEUR Workshop Proceedings. pp. 7-11
Epileptic seizure detection using constrained singular spectrum analysis and 1D-local binary patterns
Ramanna, S., Tirunagari, S. and Windridge, D. 2020. Epileptic seizure detection using constrained singular spectrum analysis and 1D-local binary patterns. Health and Technology. 10 (3), pp. 699-709. https://doi.org/10.1007/s12553-019-00395-4
Editorial to special issue on hybrid artificial intelligence and machine learning technologies in intelligent systems
Pandey, H., Bessis, N., Das, S., Windridge, D. and Chaudhary, A. 2020. Editorial to special issue on hybrid artificial intelligence and machine learning technologies in intelligent systems. Neural Computing and Applications. 32 (12), pp. 7743-7745. https://doi.org/10.1007/s00521-020-04903-w
A low-complexity trajectory privacy preservation approach for indoor fingerprinting positioning systems
Sazdar, A.M., Ghorashi, S.A., Moghtadaiee, V., Khonsari, A. and Windridge, D. 2020. A low-complexity trajectory privacy preservation approach for indoor fingerprinting positioning systems. Journal of Information Security and Applications. 53. https://doi.org/10.1016/j.jisa.2020.102515
Revisiting direct neuralisation of first-order logic
Gunn, I. and Windridge, D. 2018. Revisiting direct neuralisation of first-order logic. NeSy 2018 : Thirteenth International Workshop on Neural-Symbolic Learning and Reasoning. Prague, Czech Republic 23 - 24 Aug 2018
A generative adversarial strategy for modeling relation paths in knowledge base representation learning
Zia, T., Zahid, U. and Windridge, D. 2019. A generative adversarial strategy for modeling relation paths in knowledge base representation learning. KR2ML - Knowledge Representation and Reasoning Meets Machine Learning Workshop, NeurIPS 2019, Thirty-third Conference on Neural Information Processing Systems. Vancouver, Canada 09 - 14 Dec 2019
Fully-automated identification of imaging biomarkers for post-operative cerebellar mutism syndrome using longitudinal paediatric MRI
Spiteri, M., Guillemaut, J., Windridge, D., Avula, S., Kumar, R. and Lewis, E. 2020. Fully-automated identification of imaging biomarkers for post-operative cerebellar mutism syndrome using longitudinal paediatric MRI. Neuroinformatics. 18 (1), pp. 151-162. https://doi.org/10.1007/s12021-019-09427-w
A family of globally optimal branch-and-bound algorithms for 2D–3D correspondence-free registration
Brown, M., Windridge, D. and Guillemaut, J. 2019. A family of globally optimal branch-and-bound algorithms for 2D–3D correspondence-free registration. Pattern Recognition. 93, pp. 36-54. https://doi.org/10.1016/j.patcog.2019.04.002
Improving the adaptation process for a new smart home user
Ali, S., Augusto, J. and Windridge, D. 2019. Improving the adaptation process for a new smart home user. Bramer, M. and Petridis, M. (ed.) 39th SGAI International Conference on Artificial Intelligence. Cambridge, UK 17 - 19 Dec 2019 Springer. https://doi.org/10.1007/978-3-030-34885-4_32
A survey of user-centred approaches for smart home transfer learning and new user home automation adaptation
Ali, M., Augusto, J. and Windridge, D. 2019. A survey of user-centred approaches for smart home transfer learning and new user home automation adaptation. Applied Artificial Intelligence. 33 (8), pp. 747-774. https://doi.org/10.1080/08839514.2019.1603784
A comprehensive classification of deep learning libraries
Pandey, H. and Windridge, D. 2019. A comprehensive classification of deep learning libraries. Yang, X.S., Sherratt, S., Dey, N. and Joshi, A. (ed.) 3rd International Congress on Information and Communication Technology. London, UK 27 - 28 Feb 2018 Singapore Springer. pp. 427-435 https://doi.org/10.1007/978-981-13-1165-9_40
Edit distance Kernelization of NP theorem proving for polynomial-time machine learning of proof heuristics
Windridge, D. and Kammueller, F. 2020. Edit distance Kernelization of NP theorem proving for polynomial-time machine learning of proof heuristics. FICC 2019: Future of Information and Communications Conference. San Francisco, USA 14 - 15 Mar 2019 Springer. pp. 271-283 https://doi.org/10.1007/978-3-030-12385-7_22
Criminal pattern identification based on modified K-means clustering
Aljrees, T., Shi, D., Windridge, D. and Wong, B. 2016. Criminal pattern identification based on modified K-means clustering. 2016 International Conference on Machine Learning and Cybernetics (ICMLC). 2, pp. 799-806. https://doi.org/10.1109/ICMLC.2016.7872990
On the intrinsic limits to representationally-adaptive machine-learning
Windridge, D. 2015. On the intrinsic limits to representationally-adaptive machine-learning. ArXiv e-prints.
Proteomic identification and characterization of hepatic glyoxalase 1 dysregulation in non-alcoholic fatty liver disease
Spanos, C., Maldonado, E., Fisher, C., Leenutaphong, P., Oviedo-Orta, E., Windridge, D. and Salguero, F. 2018. Proteomic identification and characterization of hepatic glyoxalase 1 dysregulation in non-alcoholic fatty liver disease. Proteome Science. 16 (1). https://doi.org/10.1186/s12953-018-0131-y
Data governance in the health industry: investigating data quality dimensions within a big data context
Juddoo, S., George, C., Duquenoy, P. and Windridge, D. 2018. Data governance in the health industry: investigating data quality dimensions within a big data context. Applied System Innovation. 1 (4), pp. 1-16. https://doi.org/10.3390/asi1040043
Quantum error-correcting output codes
Windridge, D., Mengoni, R. and Nagarajan, R. 2018. Quantum error-correcting output codes. International Journal of Quantum Information. 16 (8). https://doi.org/10.1142/S0219749918400038
Representational fluidity in embodied (artificial) cognition
Windridge, D. and Thill, S. 2018. Representational fluidity in embodied (artificial) cognition. Biosystems. 172, pp. 9-17. https://doi.org/10.1016/j.biosystems.2018.07.007
An improved block matching algorithm for motion estimation in video sequences and application in robotics
Bhattacharjee, K., Kumar, S., Pandey, H., Pant, M., Windridge, D. and Chaudhary, A. 2018. An improved block matching algorithm for motion estimation in video sequences and application in robotics. Computers & Electrical Engineering. 68, pp. 92-106. https://doi.org/10.1016/j.compeleceng.2018.03.045
Addressing VAST 2016 mini challenge 2 with POLAR kermode, classifier, excel on a power wall and data timelines
Attfield, S., Hewitt, D., Xu, K., Passmore, P., Wagstaff, A., Phillips, G., Windridge, D., Dash, G., Chapman, R. and Mason, L. 2016. Addressing VAST 2016 mini challenge 2 with POLAR kermode, classifier, excel on a power wall and data timelines. IEEE VAST Challenge 2016. Baltimore, MD, USA 23 Oct 2016
Quantum Bootstrap Aggregation
Windridge, D. and Nagarajan, R. 2017. Quantum Bootstrap Aggregation. de Barros, J., Coecke, B. and Pothos, E. (ed.) Quantum Interaction. San Francisco, CA, USA 20 - 22 Jul 2016 Springer. pp. 115-121 https://doi.org/10.1007/978-3-319-52289-0_9
Hamming distance kernelisation via topological quantum computation
Di Pierro, A., Mengoni, R., Nagarajan, R. and Windridge, D. 2017. Hamming distance kernelisation via topological quantum computation. Martín-Vide C., Neruda R. and Vega-Rodríguez M. (ed.) 6th International Conference on the Theory and Practice of Natural Computing. Prague, Czech Republic 18 - 20 Dec 2017 Springer. pp. 269-280 https://doi.org/10.1007/978-3-319-71069-3_21
Emergent intentionality in perception-action subsumption hierarchies
Windridge, D. 2017. Emergent intentionality in perception-action subsumption hierarchies. Frontiers in Robotics and AI. 4. https://doi.org/10.3389/frobt.2017.00038
Movement correction in DCE-MRI through windowed and reconstruction dynamic mode decomposition
Tirunagari, S., Poh, N., Wells, K., Bober, M., Gorden, I. and Windridge, D. 2017. Movement correction in DCE-MRI through windowed and reconstruction dynamic mode decomposition. Machine Vision and Applications. 28 (3-4), pp. 393-407. https://doi.org/10.1007/s00138-017-0835-5
A generalised framework for saliency-based point feature detection
Brown, M., Windridge, D. and Guillemaut, J. 2017. A generalised framework for saliency-based point feature detection. Computer Vision and Image Understanding. 157, pp. 117-137. https://doi.org/10.1016/j.cviu.2016.09.008
Post-operative pediatric cerebellar mutism syndrome and its association with hypertrophic olivary degeneration
Avula, S., Spiteri, M., Kumar, R., Lewis, E., Harave, S., Windridge, D., Ong, C. and Pizer, B. 2016. Post-operative pediatric cerebellar mutism syndrome and its association with hypertrophic olivary degeneration. Quantitative Imaging in Medicine and Surgery. 6 (5), pp. 535-544. https://doi.org/10.21037/qims.2016.10.11
Classification of fibroglandular tissue distribution in the breast based on radiotherapy planning CT
Juneja, P., Evans, P., Windridge, D. and Harris, E. 2016. Classification of fibroglandular tissue distribution in the breast based on radiotherapy planning CT. BMC Medical Imaging. 16 (1). https://doi.org/10.1186/s12880-016-0107-2
Criminal pattern identification based on modified K-means clustering
Aljrees, T., Shi, D., Windridge, D. and Wong, B. 2016. Criminal pattern identification based on modified K-means clustering. 2016 International Conference on Machine Learning and Cybernetics. Jeju, South Korea 10 - 13 Jul 2016 IEEE. pp. 799-806 https://doi.org/10.1109/ICMLC.2016.7872990
Can DMD obtain a scene background in color?
Tirunagari, S., Poh, N., Bober, M. and Windridge, D. 2016. Can DMD obtain a scene background in color? 2016 International Conference on Image, Vision and Computing (ICIVC). Portsmouth, UK 03 - 05 Aug 2016 pp. 46-50 https://doi.org/10.1109/ICIVC.2016.7571272
Kernel combination via debiased object correspondence analysis
Windridge, D. and Yan, F. 2016. Kernel combination via debiased object correspondence analysis. Information Fusion. 27, pp. 228-239. https://doi.org/10.1016/j.inffus.2015.02.002
Identifying quantitative imaging features of posterior fossa syndrome in longitudinal MRI
Spiteri, M., Windridge, D., Avula, S., Kumar, R. and Lewis, E. 2015. Identifying quantitative imaging features of posterior fossa syndrome in longitudinal MRI. Journal of Medical Imaging. 2 (4), p. 044502. https://doi.org/10.1117/1.JMI.2.4.044502
Identifying similar patients using self-organising maps: a case study on type-1 diabetes self-care survey responses
Tirunagari, S., Poh, N., Hu, G. and Windridge, D. 2015. Identifying similar patients using self-organising maps: a case study on type-1 diabetes self-care survey responses. https://doi.org/10.48550/arXiv.1503.06316
Longitudinal MRI assessment: the identification of relevant features in the development of posterior fossa syndrome in children
Spiteri, M., Lewis, E., Windridge, D. and Avula, S. 2015. Longitudinal MRI assessment: the identification of relevant features in the development of posterior fossa syndrome in children. Medical imaging 2015: Computer-Aided Diagnosis. Orlando, Florida, United States 21 Feb 2015 Society of Photo-optical Instrumentation Engineers. https://doi.org/10.1117/12.2081591
Windowed DMD as a microtexture descriptor for finger vein counter-spoofing in biometrics
Tirunagari, S., Poh, N., Bober, M. and Windridge, D. 2015. Windowed DMD as a microtexture descriptor for finger vein counter-spoofing in biometrics. 2015 IEEE International Workshop on Information Forensics and Security (WIFS). Rome, Italy 16 - 19 Nov 2015 Institute of Electrical and Electronics Engineers (IEEE). pp. 1-6 https://doi.org/10.1109/WIFS.2015.7368599
Globally optimal 2D-3D registration from points or lines without correspondences
Brown, M., Windridge, D. and Guillemaut, J. 2015. Globally optimal 2D-3D registration from points or lines without correspondences. 2015 IEEE International Conference on Computer Vision (ICCV). Santiago, Chile 07 - 13 Dec 2015 Institute of Electrical and Electronics Engineers (IEEE). pp. 2111-2119 https://doi.org/10.1109/ICCV.2015.244
A generalisable framework for saliency-based line segment detection
Brown, M., Windridge, D. and Guillemaut, J. 2015. A generalisable framework for saliency-based line segment detection. Pattern Recognition. 48 (12), pp. 3993-4011. https://doi.org/10.1016/j.patcog.2015.06.015
A novel Markov logic rule induction strategy for characterizing sports video footage
Windridge, D., Kittler, J., De Campos, T., Yan, F., Christmas, W. and Khan, A. 2015. A novel Markov logic rule induction strategy for characterizing sports video footage. IEEE MultiMedia. 22 (2), pp. 24-35. https://doi.org/10.1109/MMUL.2014.36
Artificial co-drivers as a universal enabling technology for future intelligent vehicles and transportation systems
Da Lio, M., Biral, F., Bertolazzi, E., Galvani, M., Bosetti, P., Windridge, D., Saroldi, A. and Tango, F. 2015. Artificial co-drivers as a universal enabling technology for future intelligent vehicles and transportation systems. IEEE Transactions on Intelligent Transportation Systems. 16 (1), pp. 244-263. https://doi.org/10.1109/TITS.2014.2330199
Breast cancer data analytics with missing values: a study on ethnic, age and income groups
Tirunagari, S., Poh, N., Abdulrahman, H., Nemmour, N. and Windridge, D. 2015. Breast cancer data analytics with missing values: a study on ethnic, age and income groups. ArXiv e-prints: Quantitative Biology > Quantitative Methods. https://doi.org/10.48550/arXiv.1503.03680
Detection of face spoofing using visual dynamics
Tirunagari, S., Poh, N., Windridge, D., Iorliam, A., Suki, N. and Ho, A. 2015. Detection of face spoofing using visual dynamics. IEEE Transactions on Information Forensics and Security. 10 (4), pp. 762-777. https://doi.org/10.1109/TIFS.2015.2406533
Multilevel Chinese takeaway process and label-based processes for rule induction in the context of automated sports video annotation
Khan, A., Windridge, D. and Kittler, J. 2014. Multilevel Chinese takeaway process and label-based processes for rule induction in the context of automated sports video annotation. IEEE Transactions on Cybernetics. 44 (10), pp. 1910-1923. https://doi.org/10.1109/TCYB.2014.2299955
Domain anomaly detection in machine perception: a system architecture and taxonomy
Kittler, J., Christmas, W., De Campos, T., Windridge, D., Yan, F., Illingworth, J. and Osman, M. 2014. Domain anomaly detection in machine perception: a system architecture and taxonomy. IEEE Transactions on Pattern Analysis and Machine Intelligence. 36 (5), pp. 845-859. https://doi.org/10.1109/TPAMI.2013.209
A saliency-based framework for 2D-3D registration
Brown, M., Guillemaut, J. and Windridge, D. 2014. A saliency-based framework for 2D-3D registration. 9th International Conference on Computer Vision Theory and Applications (VISAPP 2014). Lisbon, Portugal 05 - 08 Jan 2014 SCITEPRESS - Science and Technology Publications. pp. 265-273 https://doi.org/10.5220/0004675402650273
Supervised selective kernel fusion for membrane protein prediction
Tatarchuk, A., Sulimova, V., Torshin, I., Mottl, V. and Windridge, D. 2014. Supervised selective kernel fusion for membrane protein prediction. Comin, M., Käll, L., Marchiori, E., Ngom, A. and Rajapakse, J. (ed.) 9th IAPR International Conference Pattern Recognition in Bioinformatics (PRIB 2014). Stockholm, Sweden 21 - 23 Aug 2014 Springer. pp. 98-109 https://doi.org/10.1007/978-3-319-09192-1_9
Challenges in designing an online healthcare platform for personalised patient analytics
Poh, N., Tirunagari, S. and Windridge, D. 2014. Challenges in designing an online healthcare platform for personalised patient analytics. 2014 IEEE Symposium on Computational Intelligence in Big Data (CIBD). Orlando, FL., USA 09 - 12 Dec 2014 IEEE. pp. 1-6 https://doi.org/10.1109/CIBD.2014.7011526
Patient level analytics using self-organising maps: a case study on type-1 diabetes self-care survey responses
Tirunagari, S., Poh, N., Aliabadi, K., Windridge, D. and Cooke, D. 2014. Patient level analytics using self-organising maps: a case study on type-1 diabetes self-care survey responses. 2014 IEEE Symposium on Computational Intelligence and Data Mining (CIDM). Orlando, FL., USA 09 - 12 Dec 2014 Institute of Electrical and Electronics Engineers (IEEE). pp. 304-309 https://doi.org/10.1109/CIDM.2014.7008682
Non-enumerative cross validation for the determination of structural parameters in feature-selective SVMs
Chernousova, E., Levdik, P., Tatarchuk, A., Mottl, V. and Windridge, D. 2014. Non-enumerative cross validation for the determination of structural parameters in feature-selective SVMs. 22nd International Conference on Pattern Recognition ICPR 2014. Stockholm, Sweden 24 - 28 Aug 2014 Institute of Electrical and Electronics Engineers (IEEE). pp. 3654-3659 https://doi.org/10.1109/ICPR.2014.628
Linear regression via elastic net: non-enumerative leave-one-out verification of feature selection
Chernousova, E., Razin, N., Krasotkina, O., Mottl, V. and Windridge, D. 2014. Linear regression via elastic net: non-enumerative leave-one-out verification of feature selection. in: Aleskerov, F., Goldengorin, B. and Pardalos, P. (ed.) Clusters, Orders, and Trees: Methods and Applications: In Honor of Boris Mirkin's 70th Birthday New York Springer.
Automatic annotation of tennis games: an integration of audio, vision, and learning
Yan, F., Kittler, J., Windridge, D., Christmas, W., Mikolajczyk, K., Cox, S. and Huang, Q. 2014. Automatic annotation of tennis games: an integration of audio, vision, and learning. Image and Vision Computing. 32 (11), pp. 896-903. https://doi.org/10.1016/j.imavis.2014.08.004
A kernel-based framework for medical big-data analytics
Windridge, D. and Bober, M. 2014. A kernel-based framework for medical big-data analytics. in: Interactive Knowledge Discovery and Data Mining in Biomedical Informatics: State-of-the-Art and Future Challenges Springer. pp. 197-208
High throughput screening for mammography using a human-computer interface with rapid serial visual presentation (RSVP)
Abbey, C., Hope, C., Sterr, A., Elangovan, P., Geades, N., Windridge, D., Young, K., Wells, K. and Mello-Thoms, C. 2013. High throughput screening for mammography using a human-computer interface with rapid serial visual presentation (RSVP). SPIE Proceedings Vol. 8673. https://doi.org/10.1117/12.2007557
Looking to score: the dissociation of goal influence on eye movement and meta-attentional allocation in a complex dynamic natural scene
Taya, S., Windridge, D. and Osman, M. 2012. Looking to score: the dissociation of goal influence on eye movement and meta-attentional allocation in a complex dynamic natural scene. PLoS ONE. 7 (6). https://doi.org/10.1371/journal.pone.0039060
Addressing missing values in kernel-based multimodal biometric fusion using neutral point substitution
Poh, N., Windridge, D., Mottl, V., Tatarchuk, A. and Eliseyev, A. 2010. Addressing missing values in kernel-based multimodal biometric fusion using neutral point substitution. IEEE Transactions on Information Forensics and Security. 5 (3), pp. 461-469. https://doi.org/10.1109/TIFS.2010.2053535