Faculty of Science and Technology, Middlesex University

Artificial Intelligence Research Group

Highlights by Area

Medical Imaging


Multi-disciplinary Trends in Artificial Intelligence

Identification of human papillomavirus from super resolution microscopic images generated using deep learning architectures


Gao, X., Wen, S., Li, D., Liu, W., Xiong, J., Xu, B., Liu, J., Zhang, H. and Liu, X. 2022. . in: Wani, M. and Palade, V. (ed.) Deep Learning Applications, Volume 4 Springer.

Deep Learning Applications, Volume 4 Springer.
Multi-disciplinary Trends in Artificial Intelligence

Visual attribution using Adversarial Latent Transformations


Tehseen Zia, Abdul Wahab, David Windridge, Santosh Tirunagari, Nauman Bashir Bhatti,

Computers in Biology and Medicine, Volume 166, 2023

Machine Learning - Information Science


Multi-disciplinary Trends in Artificial Intelligence

Theory of Information and its Value


Editors: Roman V. Belavkin, Panos M. Pardalos, Jose C. Principe

Authors: Ruslan L. Stratonovich

Theory of Information and its Value, Book © 2020
Multi-disciplinary Trends in Artificial Intelligence

Value of Information in the Mean-Square Case and Its Application to the Analysis of Financial Time-Series Forecast


Roman V. Belavkin, Panos Pardalos & Jose Principe

International Conference on Learning and Intelligent Optimization, LION 2022: Learning and Intelligent Optimization pp 549–563

Artificial Inteligence Applications


Multi-disciplinary Trends in Artificial Intelligence

Multi-disciplinary Trends in Artificial Intelligence


Editors: Raghava Morusupalli, Teja Santosh Dandibhotla, Vani Vathsala Atluri, David Windridge, Pawan Lingras, Venkateswara Rao Komati

16th International Conference, MIWAI 2023, Hyderabad, India, July 21–22, 2023, Proceedings

Quantum Machine Learning


Multi-disciplinary Trends in Artificial Intelligence

Resource saving via ensemble techniques for quantum neural network


Editors: Massimiliano Incudini, Michele Grossi, Andrea Ceschini, Antonio Mandarino, Massimo Panella, Sofia Vallecorsa & David Windridge

Incudini, M., Grossi, M., Ceschini, A. et al. Resource saving via ensemble techniques for quantum neural networks. Quantum Mach. Intell. 5, 39 (2023). https://doi.org/10.1007/s42484-023-00126-z
Multi-disciplinary Trends in Artificial Intelligence

The Quantum Path Kernel: A Generalized Neural Tangent Kernel for Deep Quantum Machine Learning


Massimiliano Incudini, Michele Grossi, Antonio Mandarino, Sofia Vallecorsa, Alessandra Di Pierro, & David Windridge

M. Incudini, et al.,"The Quantum Path Kernel: A Generalized Neural Tangent Kernel for Deep Quantum Machine Learning" in IEEE Transactions on Quantum Engineering, vol. 4, no. 01, pp. 1-16, 2023. doi: 10.1109/TQE.2023.3287736

Selected Publications


2023


  1. Application of mesh morphing techniques in modelling 3D objects Gao, Xiaohong and Hassan, Mustafa 2010. Application of mesh morphing techniques in modelling 3D objects. Annual International Conference on Computer Games Multimedia and Allied Technology (CGAT 2010). Singapore 06 - 07 Apr 2010 Global Science and Technology Forum. https://doi.org/10.5176/978-981-08-5480-5_048
  2. 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. https://doi.org/10.1016/j.compbiomed.2023.107521
  3. Malignant Mesothelioma subtyping via sampling driven multiple instance prediction on tissue image and cell morphology data Eastwood, M., Marc, S., Gao, X., Sailem, H., Offman, J., Karteris, E., Montero Fernandez, A., Jonigk, D., Cookson, W., Moffatt, M., Popat, S., Minhas, F. and Robertus, J. 2023. Malignant Mesothelioma subtyping via sampling driven multiple instance prediction on tissue image and cell morphology data. Artificial Intelligence in Medicine. 143. https://doi.org/10.1016/j.artmed.2023.102628
  4. 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
  5. Value of information in the mean-square case and its application to the analysis of financial time-series forecast, Belavkin, R., Pardalos, P. and Principe, J. 2023. Value of information in the mean-square case and its application to the analysis of financial time-series forecast. in: Learning and Intelligent Optimization Springer Nature. pp. 1-15
  6. Machine learning in pediatrics: Evaluating challenges, opportunities, and explainability Balla, Y., Tirunagari, S. and Windridge, D. 2023. Machine learning in pediatrics: Evaluating challenges, opportunities, and explainability. Indian Pediatrics. https://doi.org/S097475591600533
  7. 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

2022


  1. Identification of human papillomavirus from super resolution microscopic images generated using deep learning architectures Gao, X., Wen, S., Li, D., Liu, W., Xiong, J., Xu, B., Liu, J., Zhang, H. and Liu, X. 2022. Identification of human papillomavirus from super resolution microscopic images generated using deep learning architectures. in: Wani, M. and Palade, V. (ed.) Deep Learning Applications, Volume 4 Springer.
  2. Endoscopic image analysis using Deep Convolutional GAN and traditional data Auzine, M., Khan, M., Baichoo, S., Gooda Sahib, N., Gao, X. and Bissoonauth-Daiboo, P. 2022. Endoscopic image analysis using Deep Convolutional GAN and traditional data. International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME). Maldives 16 - 18 Nov 2022 IEEE.
  3. 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
  4. 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
  5. A user-guided personalization methodology to facilitate new smart home occupancy Ali, M., Augusto, J., Windridge, D. and Ward, E. 2022. A user-guided personalization methodology to facilitate new smart home occupancy. Universal Access in the Information Society. https://doi.org/10.1007/s10209-022-00883-x
  6. Value of information in the binary case and confusion matrix Belavkin, R., Pardalos, P. and Principe, J. 2022. Value of information in the binary case and confusion matrix. The 41st International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering. Paris, France 18 - 22 Jul 2022 MDPI. pp. 1-9 https://doi.org/10.3390/psf2022005008
  7. 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
  8. 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

2021


  1. COVID-VIT: classification of Covid-19 from 3D CT chest images based on vision transformer model Gao, X., Khan, M., Hui, R., Tian, Z., Qian, Y., Gao, A. and Baichoo, S. 2022. COVID-VIT: classification of Covid-19 from 3D CT chest images based on vision transformer model. 3rd International Conference on Next Generation Computing Applications (NextComp). Mauritius 06 - 08 Oct 2022 IEEE. https://doi.org/10.1109/NextComp55567.2022.9932246
  2. COVID-CBR: a deep learning architecture featuring case-based reasoning for classification of COVID-19 from chest x-ray images Gao, X. and Gao, A. 2021. COVID-CBR: a deep learning architecture featuring case-based reasoning for classification of COVID-19 from chest x-ray images. 20th IEEE ICMLA 2021. Virtual online 13 - 16 Dec 2021 IEEE. pp. 1319-1324 https://doi.org/10.1109/ICMLA52953.2021.00214
  3. 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
  4. 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
  5. 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

2020


  1. Transfer learning for endoscopy disease detection and segmentation with mask-RCNN benchmark architecture Rezvy, S., Zebin, T., Pang, W., Taylor, S. and Gao, X. 2020. Transfer learning for endoscopy disease detection and segmentation with mask-RCNN benchmark architecture. EndoCV2020. Iowa City, United States 03 Apr 2020 pp. 68-72
  2. Case-based reasoning of a deep learning network for prediction of early stage of oesophageal cancer Gao, X., Braden, B., Zhang, L., Taylor, S., Pang, W. and Petridis, M. 2020. Case-based reasoning of a deep learning network for prediction of early stage of oesophageal cancer. 24th UK Symposium on Case-Based Reasoning (UKCBR 2019). Cambridge, UK 17 Dec 2019 BCS SGAI: The Specialist Group on Artificial Intelligence. pp. 1-12
  3. 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
  4. 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). SGAI 2020. Cambridge, UK 15 - 17 Dec 2020 Springer Nature. https://doi.org/10.1007/978-3-030-63799-6_1
  5. A low-complexity trajectory privacy preservation approach for indoor fingerprinting positioning systems Sazdar, A., Ghorashi, S., 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
  6. 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

2019


  1. 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
  2. Towards real-time detection of squamous pre-cancers from oesophageal endoscopic videos Gao, X., Braden, B., Taylor, S. and Pang, W. 2019. Towards real-time detection of squamous pre-cancers from oesophageal endoscopic videos. ICMLA 2019. Boca Raton, Florida, USA 16 - 19 Dec 2019 IEEE. pp. 1606-1612 https://doi.org/10.1109/ICMLA.2019.00264
  3. Patch-based deep learning approaches for artefact detection of endoscopic images Gao, X. and Qian, Y. 2019. Patch-based deep learning approaches for artefact detection of endoscopic images. Endoscopic artefact detection challenge 2019 (EAD2019). Venice, Italy 08 Apr 2019 CEUR Workshop Proceedings.
  4. 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 (AI-2019).. Cambridge, UK. 17 - 19 Dec 2019 Springer. pp. 421-434 https://doi.org/10.1007/978-3-030-34885-4_32

2018


  1. 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
  2. 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
  3. 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
  4. Analysing TB severity levels with an enhanced deep residual learning– depth-resnet Gao, X., James-Reynolds, C. and Currie, E. 2018. Analysing TB severity levels with an enhanced deep residual learning– depth-resnet. ImageCLEF ImageCLEFtuberculosis competition. Avignon, France 10 - 14 Sep 2018 CEUR-WS.
  5. Environmental pleiotropy and demographic history direct adaptation under antibiotic selection Gifford, D., Krašovec, R., Aston, E., Belavkin, R., Channon, A. and Knight, C. 2018. Environmental pleiotropy and demographic history direct adaptation under antibiotic selection. Heredity. 121 (5), pp. 438-448. https://doi.org/10.1038/s41437-018-0137-3
  6. Segmentation of brain lesions from CT images based on deep learning techniques Gao, X. and Qian, Y. 2018. Segmentation of brain lesions from CT images based on deep learning techniques. Gimi, B. and Krol, A. (ed.) SPIE Medical Imaging 2018: Biomedical Applications in Molecular, Structural, and Functional Imaging. Houston, Texas, United States 10 - 15 Feb 2018 Society of Photo-optical Instrumentation Engineers (SPIE). https://doi.org/10.1117/12.2286844
  7. Data governance in the health industry: investigating data quality dimensions within a big data context Juddo, 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
  8. Relation between the Kantorovich-Wasserstein metric and the Kullback-Leibler divergence Belavkin, R. 2018. Relation between the Kantorovich-Wasserstein metric and the Kullback-Leibler divergence. IGAIA IV 2016: Information Geometry and Its Applications. Liblice, Czech Republic 12 - 17 Jun 2016 Springer International Publishing. https://doi.org/10.1007/978-3-319-97798-0_15
  9. 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), p. 1840003. https://doi.org/10.1142/s0219749918400038
  10. Prediction of multidrug-resistant TB from CT pulmonary images based on deep learning techniques Gao, X. and Qian, Y. 2018. Prediction of multidrug-resistant TB from CT pulmonary images based on deep learning techniques. Molecular Pharmaceutics. 15 (10), pp. 4326-4335. https://doi.org/10.1021/acs.molpharmaceut.7b00875
  11. A genetic deep learning model for electrophysiological soft robotics Pandey, H. and Windridge, D. 2018. A genetic deep learning model for electrophysiological soft robotics. 8th International Workshop on Soft Computing Application. University of Arad, Romania 13 - 15 Sep 2018 Springer.
  12. 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
  13. 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

2017


  1. 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. 6th International Conference on the Theory and Practice of Natural Computing (TPNC 2017. Prague, Czech Republic 18 - 20 Dec 2017 Springer. https://doi.org/10.1007/978-3-319-71069-3_21
  2. Critical mutation rate has an exponential dependence on population size for eukaryotic-length genomes with crossover Aston, E., Channon, A., Belavkin, R., Gifford, D., Krašovec, R. and Knight, C. 2017. Critical mutation rate has an exponential dependence on population size for eukaryotic-length genomes with crossover. Scientific Reports. 7 (1). https://doi.org/10.1038/s41598-017-14628-x
  3. 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
  4. Quantum Bootstrap Aggregation Windridge, D. and Nagarajan, R. 2017. Quantum Bootstrap Aggregation. 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
  5. Application of deep learning neural network for classification of TB lung CT images based on patches Gao, X. and Qian, Y. 2017. Application of deep learning neural network for classification of TB lung CT images based on patches. ImageCLEF / LifeCLEF - Multimedia Retrieval in CLEF: CLEF 2017: Conference and Labs of the Evaluation Forum. Dublin, Ireland 11 - 14 Sep 2017 CEUR Workshop Proceedings.
  6. Classification of CT brain images based on deep learning networks Gao, X., Hui, R. and Tian, Z. 2017. Classification of CT brain images based on deep learning networks. Computer methods and programs in biomedicine. 138 (2017), pp. 49-56. https://doi.org/10.1016/j.cmpb.2016.10.007

2016


  1. A deep learning based approach to classification of CT brain images Gao, X. and Hui, R. 2016. A deep learning based approach to classification of CT brain images. SAI Computing Conference 2016. London, UK 13 - 15 Jul 2016 Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/sai.2016.7555958
  2. A new approach to image enhancement for the visually impaired Gao, X. and Loomes, M. 2016. A new approach to image enhancement for the visually impaired. EI 2016: IS&T International Symposium on Electronic Imaging 2016 - Color Imaging XXI: Displaying, Processing, Hardcopy, and Applications. San Francisco, CA, USA 14 - 18 Feb 2016 Society for Imaging Science and Technology. pp. 1-7 https://doi.org/10.2352/ISSN.2470-1173.2016.20.COLOR-325
  3. 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 Post-operative pediatric cerebellar mutism syndrome and its association with hypertrophic olivary degeneration
  4. 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
  5. 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
  6. 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
  7. 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). Jeju, South Korea 10 - 13 Jul 2016 Institute of Electrical and Electronics Engineers (IEEE). pp. 799-806 https://doi.org/10.1109/ICMLC.2016.7872990
  8. Monotonicity of fitness landscapes and mutation rate control Belavkin, R., Channon, A., Aston, E., Aston, J., Krašovec, R. and Knight, C. 2016. Monotonicity of fitness landscapes and mutation rate control. Journal of Mathematical Biology. 73 (6-7), pp. 1491-1524. https://doi.org/10.1007/s00285-016-0995-3
  9. 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

2015


  1. Advancing ambient assisted living with caution Huyck, C., Augusto, J., Gao, X. and Botia, J. 2015. Advancing ambient assisted living with caution. in: Helfert, M., Holzinger, A., Ziefle, M., Fred, A., O'Donoghue, J. and Röcker, C. (ed.) Information and Communication Technologies for Ageing Well and e-Health: First International Conference, ICT4AgeingWell 2015, Lisbon, Portugal, May 20-22, 2015. Revised Selected Papers Springer.
  2. The application of KAZE features to the classification echocardiogram videos Li, W., Qian, Y., Loomes, M. and Gao, X. 2015. The application of KAZE features to the classification echocardiogram videos. First International Workshop Multimodal Retrieval in the Medical Domain (MRMD 2015). Vienna, Austria 29 Mar 2015 Springer Verlag. pp. 61-72 https://doi.org/10.1007/978-3-319-24471-6_6
  3. 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
  4. 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
  5. 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
  6. 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
  7. 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
  8. Asymmetric topologies on statistical manifolds Belavkin, R. 2015. Asymmetric topologies on statistical manifolds. GSI 2015: 2nd International Conference on Geometric Science of Information. Palaiseau, France 28 - 30 Oct 2015 Springer. https://doi.org/10.1007/978-3-319-25040-3_23
  9. 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
  10. On the intrinsic limits to representationally-adaptive machine-learning Windridge, D. 2015. On the intrinsic limits to representationally-adaptive machine-learning. ArXiv e-prints.
  11. 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
  12. Artificial co-drivers as a universal enabling technology for future intelligent vehicles and transportation systems Da Lio, M., Biral, F., Bosetti, P. and Windridge, D. 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.
  13. 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

2014


  1. 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 Institute of Electrical and Electronics Engineers (IEEE). pp. 1-6 https://doi.org/10.1109/CIBD.2014.7011526
  2. 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
  3. 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
  4. 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
  5. 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.
  6. Asymmetry of risk and value of information Belavkin, R. 2014. Asymmetry of risk and value of information. in: Vogiatzis, C., Walteros, J. and Pardalos, P. (ed.) Dynamics of Information Systems : Computational and Mathematical Challenges Springer.
  7. On variational definition of quantum entropy Belavkin, R. 2014. On variational definition of quantum entropy. 34th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering (MaxEnt 2014). Clos Lucé, Amboise, France 21 - 26 Sep 2014 AIP Publishing. https://doi.org/10.1063/1.4905979
  8. 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. pp. 265-273 https://doi.org/10.5220/0004675402650273
  9. 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. pp. 265-273 https://doi.org/10.5220/0004675402650273
  10. Feature-wise representation for both still and motion 3D medical images Gao, X. 2014. Feature-wise representation for both still and motion 3D medical images. 2014 IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI). San Diego, USA 06 - 09 Apr 2014 IEEE. pp. 1-4 https://doi.org/10.1109/SSIAI.2014.6806014
  11. 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. Pattern Analysis and Machine Intelligence, IEEE Transactions on. 36 (5), pp. 845-859. https://doi.org/10.1109/TPAMI.2013.209
  12. 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.
  13. 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. Pattern Analysis and Machine Intelligence, IEEE Transactions on. 36 (5), pp. 845-859. https://doi.org/10.1109/TPAMI.2013.209