Prof Xiaohong Gao
| Name | Prof Xiaohong Gao |
|---|---|
| Job title | Professor of Vision Imaging |
| Research institute | |
| Primary appointment | Computer Science |
| Email address | x.gao@mdx.ac.uk |
| ORCID | https://orcid.org/0000-0002-8103-6624 |
| Contact category | Academic staff |
Biography
Biography
Prof. Gao is currently a world leading academic in AI in medical
applications with the evidence of very high impact publications such as Information
Fusion 2023 (IF17.564), Nature Cell
Reports Medicine 2023 (IF16.988), Medical
Image Analysis 2021 (IF8.79) and Artificial
Intelligence in Medicine 2023 (IF7.5), working
with elite scientific consortiums with world top 100 universities, e.g.
University of Oxford, Imperial College and University of Warwick on joint
research projects funded by Cancer Research UK (CRUK) and Asthma+Lung UK, and
playing a leading role in enhancing research into teaching for global
education by implementing diversity policies and inclusion with Women in STEM
(WiS) MSc Scholarships, WiS post-doc Fellowships and Early Career
Fellowships (ECFs), all funded by British council (BC) (PI). Gao obtained her BSc degree in Applied Mathematics with 1st Class
in Liaoning University China in 1984, MSc in Computer Graphics in Jilin
University China 1989 and PhD in Computer Science at Loughborough University
in Loughborough in 1994. She is currently a Fellow of Higher Education
Academy (FHEA). After her PhD study, she worked as a post-doc researcher at Imperial
College London (1995-1997) and University of Cambridge (1998-1999)
respectively. She joined Middlesex University (MU) in 1999 and has been a
Professor in Computer Vision and Imaging Science since 2013.
Teaching MSc Data Science, Data Visual Analytics MSc Data Science, Individual Project
Education and qualifications
Grants
Projects
- British Council Stem Scholarship
- British Council STEM Fellowship
- British Council Stem Scholarship
Prizes and Awards
Research outputs
Development of an ensemble CNN model with explainable AI for the classification of gastrointestinal cancer
Auzine, M., Heenaye-Mamode Khan, M., Baichoo, S., Gooda Sahib, N., Bissoonauth-Daiboo, P., Gao, X. and Heetun, Z. 2024. Development of an ensemble CNN model with explainable AI for the classification of gastrointestinal cancer. PLoS ONE. 19 (6), p. e0305628. https://doi.org/10.1371/journal.pone.0305628Texture-based 3d image retrieval for medical applications
Gao, X., Qian, Y., Hui, R., Loomes, M., Comley, R., Barn, B., Chapman, A. and Rix, J. 2010. Texture-based 3d image retrieval for medical applications. Macedo, M. (ed.) IADIS International Conference e-Health 2010. Freiburg, Germany 29 - 31 Jul 2010 IADIS. pp. 101-108An 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_12MesoGraph: automatic profiling of mesothelioma subtypes from histological images
Eastwood, M., Sailem, H., Marc, S., Gao, X., Offman, J., Karteris, E., Fernandez, A., Jonigk, D., Cookson, W., Moffatt, M., Popat, S., Minhas, F. and Robertus, J. 2023. MesoGraph: automatic profiling of mesothelioma subtypes from histological images. Cell Reports Medicine. 4 (10). https://doi.org/10.1016/j.xcrm.2023.101226Application of mesh morphing techniques in modelling 3D objects
Gao, X. and Hassan, M. 2010. Application of mesh morphing techniques in modelling 3D objects. Annual International Conference on Computer Games Multimedia and Allied Technology. Singapore 06 - 07 Apr 2010 Global Science and Technology Forum. https://doi.org/10.5176/978-981-08-5480-5_048Malignant 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.2023.102628Identification 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.Fusion of colour contrasted images for early detection of oesophageal squamous cell dysplasia from endoscopic videos in real time
Gao, X., Taylor, S., Pang, W., Hui, R., Lu, X., Oxford GI Investigators and Braden, B. 2023. Fusion of colour contrasted images for early detection of oesophageal squamous cell dysplasia from endoscopic videos in real time. Information Fusion. 92, pp. 64-79. https://doi.org/10.1016/j.inffus.2022.11.023Endoscopic image analysis using deep convolutional GAN and traditional data augmentation
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 augmentation. International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME). Maldives 16 - 18 Nov 2022 IEEE. https://doi.org/10.1109/ICECCME55909.2022.9988503COVID-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). Flic-en-Flac, Mauritius 06 - 08 Oct 2022 IEEE. https://doi.org/10.1109/NextComp55567.2022.9932246Malignant Mesothelioma subtyping of tissue images via sampling driven multiple instance prediction
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. 2022. Malignant Mesothelioma subtyping of tissue images via sampling driven multiple instance prediction. Michalowski, M., Abidi, S. and Abidi, S. (ed.) 20th International Conference on Artificial Intelligence in Medicine. Halifax, Canada 14 - 17 Jun 2022 Springer. https://doi.org/10.1007/978-3-031-09342-5_25Early detection of oesophageal cancer through colour contrast enhancement for data augmentation
Gao, X., Taylor, S., Pang, W., Lu, X. and Braden, B. 2022. Early detection of oesophageal cancer through colour contrast enhancement for data augmentation. SPIE Medical Imaging: Computer-Aided Diagnosis. San Diego, USA 21 - 24 Feb 2022 Society of Photo-optical Instrumentation Engineers. https://doi.org/10.1117/12.2611409Detection of human papillomavirus (HPV) from super resolution microscopic images applying an explainable deep learning network
Gao, X., Wen, S., Li, D., Liu, W., Xiong, J., Xu, B., Liu, J., Zhang, H. and Liu, X. 2022. Detection of human papillomavirus (HPV) from super resolution microscopic images applying an explainable deep learning network. SPIE Medical Imaging: Biomedical Applications in Molecular, Structural, and Functional Imaging. San Diego, USA 20 - 22 Feb 2022 Society of Photo-optical Instrumentation Engineers. https://doi.org/10.1117/12.2624423Gold-viral particle identification by deep learning in wide-field photon scattering parametric images
Zhao, H., Ni, B., Jin, X., Zhang, H., Hou, J., Hou, L., Marsh, J., Dong, L., Li, S., Gao, X., Shi, D., Liu, X. and Xiong, J. 2022. Gold-viral particle identification by deep learning in wide-field photon scattering parametric images. Applied Optics. 61 (2), pp. 546-553. https://doi.org/10.1364/AO.445953COVID-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/ICMLA52953.2021.00214Evaluation of GAN architectures for visualisation of HPV viruses from microscopic images
Gao, X., Wen, S., Li, D., Liu, W., Xiong, J., Xu, B., Liu, J., Zhang, H. and Liu, X. 2021. Evaluation of GAN architectures for visualisation of HPV viruses from microscopic images. 20th IEEE ICMLA 2021. Virtual online 13 - 16 Dec 2021 IEEE. pp. 829-833 https://doi.org/10.1109/ICMLA52953.2021.00137Signal denoising of viral particle in wide-field photon scattering parametric images using deep learning
Zhao, H., Ni, B., Liu, W., Jin, X., Zhang, H., Gao, X., Wen, S., Shi, D., Dong, L., Xiong, J. and Liu, X. 2022. Signal denoising of viral particle in wide-field photon scattering parametric images using deep learning. Optics Communications. 503. https://doi.org/10.1016/j.optcom.2021.127463Artificial intelligence in endoscopy: the challenges and future directions
Gao, X. and Braden, B. 2021. Artificial intelligence in endoscopy: the challenges and future directions. Artifical Intelligence in Gastrointestinal Endoscopy. 2 (4), pp. 117-126. https://doi.org/10.37126/aige.v2.i4.117Deep learning for detection and segmentation of artefact and disease instances in gastrointestinal endoscopy
Ali, S., Dmitrieva, M., Ghatwary, N., Bano, S., Polat, G., Temizel, A., Krenzer, A., Hekalo, A., Guo, Y., Matuszewski, B., Gridach, M., Voiculescu, I., Yoganand, V., Chavan, A., Raj, A., Nguyen, N., Tran, D., Huynh, L., Boutry, N., Rezvy, S., Chen, H., Choi, Y., Subramanian, A., Balasubramanian, V., Gao, X., Hu, H., Liao, Y., Stoyanov, D., Daul, C., Realdon, S., Cannizzaro, R., Lamarque, D., Tran-Nguyen, T., Bailey, A., Braden, B., East, J. and Rittscher, J. 2021. Deep learning for detection and segmentation of artefact and disease instances in gastrointestinal endoscopy. Medical Image Analysis. 70. https://doi.org/10.1016/j.media.2021.102002Characterization of deep sub-wavelength nanowells by imaging the photon state scattering spectra
Liu, W., Liu, X. and Gao, X. 2021. Characterization of deep sub-wavelength nanowells by imaging the photon state scattering spectra. Optics Express. 29 (2), pp. 1221-1231. https://doi.org/10.1364/OE.413942An enhanced deep learning architecture for classification of Tuberculosis types from CT lung images
Gao, X., Comley, R. and Khan, M. 2020. An enhanced deep learning architecture for classification of Tuberculosis types from CT lung images. ICIP 2020: 27th IEEE International Conference on Image Processing. Abu Dhabi, Unites Arab Emirates (Virtual Conference) 25 - 28 Oct 2020 IEEE. pp. 2486-2490 https://doi.org/10.1109/ICIP40778.2020.9190815Case-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. 1-12Transfer 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. 2nd International Workshop and Challenge on Computer Vision in Endoscopy. Iowa City, United States 03 Apr 2020 pp. 68-72An objective comparison of detection and segmentation algorithms for artefacts in clinical endoscopy
Ali, S., Zhou, F., Braden, B., Bailey, A., Yang, S., Cheng, G., Zhang, P., Li, X., Kayser, M., Soberanis-Mukul, R., Albarqouni, S., Wang, X., Wang, C., Watanabe, S., Oksuz, I., Ning, Q., Yang, S., Khan, M., Gao, X., Realdon, S., Loshchenov, M., Schnabel, J., East, J., Wagnieres, G., Loschenov, V., Grisan, E., Daul, C., Blondel, W. and Rittscher, J. 2020. An objective comparison of detection and segmentation algorithms for artefacts in clinical endoscopy. Scientific Reports. 10 (1), pp. 1-15. https://doi.org/10.1038/s41598-020-59413-5Towards 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.2019.00264Patch-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.Analysis of tuberculosis severity levels from CT pulmonary images based on enhanced residual deep learning architecture
Gao, X., James-Reynolds, C. and Currie, E. 2020. Analysis of tuberculosis severity levels from CT pulmonary images based on enhanced residual deep learning architecture. Neurocomputing. 392, pp. 233-244. https://doi.org/10.1016/j.neucom.2018.12.086Analysing 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. Cappellato, L., Ferro, N., Nie, J-Y. and Soulier, L. (ed.) CLEF 2018 Conference and Labs of the Evaluation Forum - ImageCLEF-Multimedia Retrieval in CLEF. Avignon, France 10 - 14 Sep 2018 CEUR-WS.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/10.1117/12.2286844Prediction 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.7b00875Application 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.A fused deep learning architecture for viewpoint classification of echocardiography
Gao, X., Li, W., Loomes, M. and Wang, L. 2017. A fused deep learning architecture for viewpoint classification of echocardiography. Information Fusion. 36, pp. 103-113. https://doi.org/10.1016/j.inffus.2016.11.007Classification 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.2016.10.007A 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 IEEE. https://doi.org/10.1109/sai.2016.7555958A 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. 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-325Advancing 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.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. pp. 61-72 https://doi.org/10.1007/978-3-319-24471-6_6Evaluation of colour appearances displaying on smartphones
Gao, X., Khodamoradi, E., Guo, L., Yang, X., Tang, S., Guo, W. and Wang, Y. 2015. Evaluation of colour appearances displaying on smartphones. Yaguchi, H., Okajima, K., Ishida, T., Araki, K., Doi, M. and Manabe, Y. (ed.) AIC 2015, Color and Image, Midterm meeting of the International Colour Association (AIC). Tokyo, Japan 19 - 22 May 2015 The Color Science Association of Japan. pp. 539-544Modelling of chromatic contrast for retrieval of wallpaper images
Gao, X., Wang, Y., Qian, Y. and Gao, A. 2015. Modelling of chromatic contrast for retrieval of wallpaper images. Color Research and Application. 40 (4), pp. 361-373. https://doi.org/10.1002/col.21897Feature-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.2014.68060143D CBIR with sparse coding for image-guided neurosurgery
Qian, Y., Hui, R. and Gao, X. 2013. 3D CBIR with sparse coding for image-guided neurosurgery. Signal Processing. 93 (6), pp. 1673-1683. https://doi.org/10.1016/j.sigpro.2012.10.020The synergy of 3D SIFT and sparse codes for classification of viewpoints from echocardiogram videos
Qian, Y., Wang, L., Wang, C. and Gao, X. 2013. The synergy of 3D SIFT and sparse codes for classification of viewpoints from echocardiogram videos. Greenspan, H., Müller, H. and Syeda-Mahmood, T. (ed.) 3rd MICCAI International Workshop on Medical Content-Based Retrieval for Clinical Decision Support. Nice, France 01 - 01 Oct 2012 Berlin, Heidelberg Springer. https://doi.org/10.1007/978-3-642-36678-9_7Cardiac motion reconstruction using LKT algorithm from 2D and 3D echocardiography
Gao, A., Li, W., Lin, C., Loomes, M. and Gao, X. 2013. Cardiac motion reconstruction using LKT algorithm from 2D and 3D echocardiography. in: IPCV'13 - The 2013 International Conference on Image Processing, Computer Vision, and Pattern Recognition CSRES Press.Retrieval of 3D medical images via their texture features
Gao, X., Qian, Y., Loomes, M., Barn, B., Comley, R., Chapman, A., Rix, J., Hui, R. and Tian, Z. 2012. Retrieval of 3D medical images via their texture features. International Journal on Advances in Software. 4 (3&4), pp. 499-509.Bridging the abridged – the diffusion of Telemedicine in Europe and China
Gao, X., Loomes, M. and Comley, R. 2012. Bridging the abridged – the diffusion of Telemedicine in Europe and China. in: Rodrigues, J., Díez, I. and Abajo, B. (ed.) Telemedicine and e-health services, policies, and applications: avancements and developments USA IGI Global. pp. 451-495The anatomy of teleneurosurgery in China
Gao, X. 2011. The anatomy of teleneurosurgery in China. International Journal of Telemedicine and Applications. 2011. https://doi.org/10.1155/2011/353405Content-based petrieval of 3D medical images
Qian, Y., Gao, X., Loomes, M., Comley, R., Barn, B., Hui, R. and Tian, Z. 2011. Content-based petrieval of 3D medical images. in: Gemert-Pijnen, L., Ossebaard, H. and Hämäläinen, P. (ed.) eTELEMED 2011, The Third International Conference on eHealth, Telemedicine, and Social Medicine IARIA. pp. 7-12The state of the art of medical imaging technology: from creation to archive and back.
Gao, X., Qian, Y. and Hui, R. 2011. The state of the art of medical imaging technology: from creation to archive and back. The Open Medical Informatics Journal. 5 (1-M8), pp. 73-85. https://doi.org/10.2174/1874431101105010073A new approach to estimation of non-isotropic scale factors for correction of MR distortion
Gao, X., Hui, R., White, A.S. and Tian, Z. 2009. A new approach to estimation of non-isotropic scale factors for correction of MR distortion. International Journal of Computer Assisted Radiology and Surgery. 4 (s1), pp. s349-s350.What ELSE? Regulation and compliance in medical imaging and medical informatics
Duquenoy, P., George, C. and Solomonides, A. 2008. What ELSE? Regulation and compliance in medical imaging and medical informatics. in: Gao, X., Müller, H., Loomes, M., Comley, R. and Luo, S. (ed.) Medical Imaging and Informatics: 2nd International Conference, MIMI 2007. Berlin Springer.Image classification based on the informative regions properties.
Gao, X., Podladchikova, L. and Shaposhnikov, D. 2003. Image classification based on the informative regions properties. in: Proceedings of PRIA-6-2002 : 6th International conference on pattern recognition and image analysis: new information technologies. MAIK Nauka/Interperiodica. pp. 439-441Telemedicine in Europe.
Gao, X. 2006. Telemedicine in Europe. ChinaPacs. Beijing 14 - 16 Apr 2006A new approach to traffic sign recognition
Gao, X., Podladchikova, L., Shaposhnikov, D., Hong, K., Batty, S., Golovan, A., Gusakova, V. and Shevtsova, N. 2002. A new approach to traffic sign recognition. in: Arabnia, H. and Mun, Y. (ed.) Proceedings of the international conference on imaging science, systems, and technology: CISST'02. Athens CSREA Press.The state of art of medical displays.
Gao, X. 2006. The state of art of medical displays. EuroPacs 2006. Trondheim, Norway 14 - 17 Jun 2006Towards archiving 3D PET brain images based on their physiological and visual content.
Gao, X., Batty, S., Clark, J., Fryer, T. and Turkheimer, F. 2002. Towards archiving 3D PET brain images based on their physiological and visual content. International conference on diagnostic imaging and analysis. Shanghai, China 18 - 20 Aug 2002Toward a robust system to monitor the head motions during PET based on facial landmarks detection: a new approach
Anishenko, S., Osimov, V., Shaposhnikov, D., Podladchikova, L., Comley, R. and Gao, X. 2008. Toward a robust system to monitor the head motions during PET based on facial landmarks detection: a new approach. Puuronen, S., Pechenizkiy, M., Tsymbal, A. and Lee, D. (ed.) 21st IEEE International Symposium on Computer-Based Medical Systems. Jyvaskyla, Finland 17 - 19 Jun 2008 IEEE Computer Society. pp. 50-52 https://doi.org/10.1109/CBMS.2008.19A fast approach to segmentation of PET brain images for extraction of features
Gao, X. and Clark, J. 2008. A fast approach to segmentation of PET brain images for extraction of features. Gao, X., Loomes, M., Comley, R., Muller, H. and Luo, S. (ed.) International Conference on Medical Imaging and Informatics (MIMI 2007). Beijing, China 14 - 16 Aug 2007 Berlin, Heidelberg Springer. https://doi.org/10.1007/978-3-540-79490-5_25Prototype system for semantic retrieval of neurological PET images
Batty, S., Clark, J., Fryer, T. and Gao, X. 2008. Prototype system for semantic retrieval of neurological PET images. Gao, X., Muller, H., Loomes, M., Comley, R. and Luo, S. (ed.) International Conference on Medical Imaging and Informatics (MIMI 2007). Beijing, China 14 - 16 Aug 2007 Berlin, Heidelberg Springer. https://doi.org/10.1007/978-3-540-79490-5_23Colour vision model-based approach for segmentation of traffic signs
Gao, X., Hong, K., Passmore, P., Podladchikova, L. and Shaposhnikov, D. 2008. Colour vision model-based approach for segmentation of traffic signs. EURASIP Journal on Image and Video Processing. 2008. https://doi.org/10.1155/2008/386705High-precision detection of facial landmarks to estimate head motions based on vision models
Gao, X., Anishenko, S., Shaposhnikov, D., Podladchikova, L., Batty, S. and Clark, J. 2007. High-precision detection of facial landmarks to estimate head motions based on vision models. Journal of computer sciences. 3 (7), pp. 528-532.Detection of head motions using a vision model
Gao, X., Shaposhnikov, D., Podladchikova, L., Batty, S. and Clark, J. 2007. Detection of head motions using a vision model. Bashshur, R. (ed.) 3rd IASTED International Conference on Telehealth. Montreal, QC, Canada 31 May - 01 Jun 2007 Anaheim, CA Acta Press. pp. 167-171Recognition of traffic signs based on their colour and shape features extracted using human vision models
Gao, X., Podladchikova, L., Shaposhnikov, D., Hong, K. and Shevtsova, N. 2006. Recognition of traffic signs based on their colour and shape features extracted using human vision models. Journal of Visual Communication and Image Representation. 17 (4), pp. 675-685. https://doi.org/10.1016/j.jvcir.2005.10.003Colour reproduction for tele-imaging systems
Gao, X. and He, P. 2006. Colour reproduction for tele-imaging systems. Computerized medical imaging and graphics. 30 (6-7), pp. 79-84.Content-based retrieval of PET images via localised anatomical texture measurements and mean activity levels
Gao, X., Batty, S., Clark, J. and Fryer, T. 2006. Content-based retrieval of PET images via localised anatomical texture measurements and mean activity levels. Computerized medical imaging and graphics. 30 (6-7), pp. 70-74.Application of vision models to traffic sign recognition
Gao, X., Shaposhnikov, D. and Podladchikova, L. 2004. Application of vision models to traffic sign recognition. Kaynak, O., Alpaydin, E., Oja, E. and Xu, L. (ed.) Artificial Neural Networks and Neural Information Processing — ICANN/ICONIP 2003. Istanbul, Turkey 26 - 29 Sep 2003 Berlin, Heidelberg Springer. https://doi.org/10.1007/3-540-44989-2_131Towards archiving wallpaper images
Gao, X., Qian, Y., Tully, T. and Hendon, Z. 2004. Towards archiving wallpaper images. in: Hamza, M. (ed.) Proceedings of the seventh IASTED international conference on computer graphics and imaging. Anaheim Acta Press. pp. 305-309Colour management in telemedicine
Gao, X. 2004. Colour management in telemedicine. Hamza, M. (ed.) 7th IASTED International Conference on Computer Graphics and Imaging. Kauai, Hawaii, United States 16 - 18 Aug 2004 Anaheim, CA Acta Press. pp. 361-364Classification of images on the basis of the properties of informative regions.
Gao, X., Shaposhnikov, D. and Podladchikova, L. 2003. Classification of images on the basis of the properties of informative regions. Pattern Recognition and Image Analysis. 13 (2), pp. 349-352.Application of the behavioural model of vision for invariant recognition of facial and traffic sign images.
Gao, X., Shaposhnikov, D., Podladchikova, L., Golovan, A., Shevtsova, N., Gusakova, V. and Gizatdinova, Y. 2003. Application of the behavioural model of vision for invariant recognition of facial and traffic sign images. in: Gulaev, Y. and Galushkin, A. (ed.) Neurocomputers and their application. [In Russian] Moscow Radiotechnics.Extraction of physiological information from 3D PET brain images.
Gao, X., Batty, S., Fryer, T., Clark, J., Turkheimer, F. and International Association of Science and Technology for Development. 2003. Extraction of physiological information from 3D PET brain images. in: Villanueva, J. (ed.) Visualization imaging and image processing. Acta Press. pp. 401-405Towards content-based retrieval for wallpaper images
Qian, Y., Tully, C., Hendon, Z. and Gao, X. 2003. Towards content-based retrieval for wallpaper images. 7th IASTED International Conference on Computer Graphics and Imaging. Kauai, Hawaii, United States 16 - 18 Aug 2004 pp. 305-309Content based retrieval of lesioned brain images
Batty, S., Blandford, A., Clark, J., Fryer, T. and Gao, X. 2002. Content based retrieval of lesioned brain images. Siegel, E. and Huang, H. (ed.) SPIE Medical Imaging 2002. San Diego, California, United States 23 - 28 Feb 2002 Bellingham Society of Photo-optical Instrumentation Engineers (SPIE). https://doi.org/10.1117/12.466997Road sign recognition by one fixation of space-variant sensor.
Gao, X., Shaposhnikov, D., Podladchikova, L., Golovan, A., Shevtsova, N. and Hong, K. 2002. Road sign recognition by one fixation of space-variant sensor. in: Gorodnich, D. and Zhang, H. (ed.) Vision Interface ’2002: proceedings Quebec Canadian Image Processing and Pattern Recognition Society.Invariant recognition of traffic signs
Gao, X., Shaposhnikov, D., Podladchikova, L., Shevtsova, N. and Golovan, A. 2002. Invariant recognition of traffic signs.Road sign recognition by means of the behavioural model of vision.
Gao, X., Golovan, A., Hong, K., Podladchikova, L. and Shevtsova, N. 2002. Road sign recognition by means of the behavioural model of vision. in: Proceedings of the third all-Russian conference on neuroinformatics [In Russian]. Moscow. pp. 63-69Extraction of features from 3D PET images.
Gao, X., Batty, S., Clark, J. and Fryer, T. 2002. Extraction of features from 3D PET images. in: Houston, A. and Zwiggelar, R. (ed.) Medical image understanding and analysis 2002. BMVA.Vision models based identification of traffic signs
Gao, X., Podladchikova, L., Shaposhnikov, D., Shevtsova, N., Hong, K., Batty, S., Golovan, A. and Gusakova, V. 2002. Vision models based identification of traffic signs. 1st European Conference on Colour Graphics, Imaging, and Vision. University of Poitiers, France 02 - 05 Apr 2002 Society for Imaging Science and Technology.Image retrieval through perceptual shape modelling.
Gao, X., Ren, M., Riley, K., Eakins, J. and Briggs, P. 2001. Image retrieval through perceptual shape modelling. London Council for Museums, Archives and Libraries.Measurement of vessel diameters on retinal for cardiovascular studies.
Gao, X., Bharath, A., Stanton, A., Hughes, A., Chapman, N. and Thom, S. 2001. Measurement of vessel diameters on retinal for cardiovascular studies. in: Claridge, E., Bamber, J. and Marlow, K. (ed.) Medical image understanding and analysis 2001. Medical Imaging Understanding and Analysis.Extraction of sagittal symmetry planes from PET images.
Gao, X., Batty, S., Clark, J., Fryer, T., Blandford, A. and International Association of Science and Technology for Development. 2001. Extraction of sagittal symmetry planes from PET images. in: Hamza, M. (ed.) Visualization, imaging and image processing: proceedings of the IASTED international conference. Calgary IASTED. pp. 428-433A method of vessel tracking for vessel diameter measurement on retinal images
Gao, X., Bharath, A., Stanton, A., Hughes, A., Chapman, N. and Thom, S. 2001. A method of vessel tracking for vessel diameter measurement on retinal images. 2001 International Conference on Image Processing. Thessaloniki, Greece 07 - 10 Oct 2001 IEEE.Computer algorithms for the automated measurement of retinal arteriolar diameters
Chapman, N., Witt, N., Gao, X., Bharath, A., Stanton, A., Thom, S. and Hughes, A. 2001. Computer algorithms for the automated measurement of retinal arteriolar diameters. British Journal of Ophthalmology. 85 (1), pp. 74-79. https://doi.org/10.1136/bjo.85.1.74Quantification and characterization of arteries in retinal images
Gao, X., Bharath, A., Stanton, A., Hughes, A., Chapman, N. and Thom, S. 2000. Quantification and characterization of arteries in retinal images. Computer Methods and Programs in Biomedicine. 63 (2), pp. 133-146. https://doi.org/10.1016/S0169-2607(00)00082-15635
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