Prof Xiaohong Gao


Prof Xiaohong Gao
NameProf Xiaohong Gao
Job titleProfessor of Vision Imaging
Research institute
Primary appointmentComputer Science
Email addressx.gao@mdx.ac.uk
ORCIDhttps://orcid.org/0000-0002-8103-6624
Contact categoryResearcher

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

PhD
01 Jul 1994
Loughborough University
Modelling of Colour Appearance
01 Jul 1989
Jilin University, China
MSc in computer graphics
01 Jul 1984
Liaoning University, China
BSc in Applied Mathematics (1st)

Grants

Next generation of leaders in responsible AI (NGARAI)

PI: 2024-2026 (£180,000)

01 Mar 2024
British Council
Machine Learning approaches to detect pre-neoplastic changes in mesothelial cells for early detection of mesothelioma using pleural fluid and liquid biopsies

Co-PI: 2023-2025 (£327,000), Lead: Imperial College Royal Brompton Hospital

01 Mar 2023
ASTHMA+Lung UK
Women in STEM Early Stage Fellowships

PI: £180,000

01 Mar 2023
British Council
Early Detection of Gastric Cancer using Deep learning

Co-PI: 2021-2024, Lead, University of Mauritius, Rs 1.479,375 (£25,000)

01 Aug 2021
Mauritius Higher Education Commission
Endo.AI: Real time automated endoscopic detection and segmentation of oesophageal squamous cell cancer in early (pre-cancerous stages.

Co-PI: £43,330/£100,000, Lead: University of Oxford.

01 Aug 2019
C68574/A29021
CRUK
Visualisation of super resolution microscopy data with structural contents using deep learning techniques

PI, £12,000

01 Aug 2019
IEC\NSFC\181557
The Royal Society UK Newton Fund
PRISM - Machine Learning for Discovery of Pre-neoplastic signature in Mesothelioma.

Co-PI, £100,000, Lead, Imperial College

01 Oct 2020
CRUK

Projects

  • British Council Stem Scholarship
  • British Council STEM Fellowship
  • British Council Stem Scholarship

Prizes and Awards

Research outputs

Texture-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. 101-108

MesoGraph: 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.2023.101226

Application 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_048

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

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.

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.2022.11.023

Endoscopic 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/ICECCME55909.2022.9988503

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). Flic-en-Flac, Mauritius 06 - 08 Oct 2022 IEEE. https://doi.org/10.1109/NextComp55567.2022.9932246

Malignant 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_25

Early 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/10.1117/12.2611409

Detection 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/10.1117/12.2624423

Gold-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.445953

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

Evaluation 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/ICMLA52953.2021.00137

Signal 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.2021.127463

Artificial 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.i4.117

Deep 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.2021.102002

Characterization 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.413942

An 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/ICIP40778.2020.9190815

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

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. 2nd International Workshop and Challenge on Computer Vision in Endoscopy. Iowa City, United States 03 Apr 2020 pp. 68-72

An 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/s41598-020-59413-5

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

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.

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.2018.12.086

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.

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

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

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.

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.2016.11.007

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

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 IEEE. https://doi.org/10.1109/sai.2016.7555958

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. 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

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.

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_6

Evaluation 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. 539-544

Modelling 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.21897

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

3D 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.2012.10.020

The 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_7

Cardiac 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. 451-495

The anatomy of teleneurosurgery in China

Gao, X. 2011. The anatomy of teleneurosurgery in China. International Journal of Telemedicine and Applications. 2011. https://doi.org/10.1155/2011/353405

Content-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. 7-12

The 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/10.2174/1874431101105010073

A new approach to estimation of non-isotropic scale factors for correction of MR distortion

Gao, X., Hui, R., White, T. 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. 439-441

Telemedicine in Europe.

Gao, X. 2006. Telemedicine in Europe. ChinaPacs. Beijing 14 - 16 Apr 2006

A 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 2006

Towards 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 2002

Toward a robust system to monitor the head motions during PET based on facial landmarks detection: a new approach.

Gao, X., Podladchikova, L., Shaposhnikov, D., Comley, R., Anishenko, S. and Osimov, V. 2008. Toward a robust system to monitor the head motions during PET based on facial landmarks detection: a new approach. in: Puuronen, S. (ed.) Proceedings of the 21st IEEE international symposium on computer-based medical systems. Jyväskylä, Finland IEEE Computer Society.

A 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_25

Prototype 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_23

Colour 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/10.1155/2008/386705

High-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. 167-171

Recognition 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.2005.10.003

Colour 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_131

Towards 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. 305-309

Colour 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. 361-364

Classification 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. 401-405

Content 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/10.1117/12.466997

Road 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. 63-69

Extraction 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. 428-433

A 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.85.1.74

Quantification 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)00082-1
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