Key details
Dr Mohammad Al-Antary
Lecturer in Computer Science
Dr Mohammad Al-Antary is a Lecturer in Computer Science at the University of Greenwich and a member of the Predictive Imaging Lab and the Centre for Advanced Simulation and Modelling. His research applies artificial intelligence to medical image analysis, developing deep learning tools that support earlier and more accurate diagnosis. He works closely with clinical collaborators, so that his research addresses real problems in disease screening and treatment.
His current research covers disease detection and grading, medical image segmentation, treatment response prediction and the reliable use of AI models across different hospitals and patient populations. He also collaborates internationally on remote sensing image segmentation and network security.
Dr Al-Antary leads four undergraduate and MSc modules in machine learning, data science, object oriented programming and system administration and security. He supervises and co-supervises BSc final year projects and MSc projects and dissertations, and doctoral research. He has been teaching at Greenwich since 2018, is a Fellow of the Higher Education Academy, and previously worked in industry as an Associate Big Data Consultant.
He holds a PhD in Computing and Information Systems (Artificial Intelligence and Data Science) and an MSc (Distinction) from the University of Greenwich, and a BSc from the University of Jordan.
Responsibilities within the university
Module Leader
- COMP1861 Machine Learning (Level 6)
- COMP1752 Object Oriented Programming (Level 4)
- COMP1857 Introduction to Data Science (Level 4)
- COMP1475 System Administration and Security (Level 7, MSc)
Module Contribution
- COMP1827 Introduction to Artificial Intelligence (Level 5)
- COMP1753 Programming Foundations (Level 4)
Moderator
- COMP1858 Data Structures and Algorithms
- COMP1891 Applications in AI and Data Science
- COMP1921 Advanced Topics in Data Science and AI
- Final year projects for collaborative (partner institution) programmes
Supervisor and Co-supervisor
- BSc final year projects
- MSc projects and dissertations
- Doctoral (PhD) research
Academic Tutor
Undergraduate and MSc students
Awards
- PyTorch Scholarship Challenge, Facebook Artificial Intelligence and Udacity, 2018 to 2019
Recognition
- Fellow of the Higher Education Academy (FHEA), Advance HE, December 2024
- Co-Guest Editor, MDPI Electronics Special Issue, 2026
- Member, British Machine Vision Association (BMVA)
- Peer reviewer for IEEE Transactions on Consumer Electronics, IEEE Access, ACM Distributed Ledger Technologies: Research and Practice and a range of other IEEE, ACM and MDPI journals
Research / Scholarly interests
Dr Al-Antary's research centres on artificial intelligence for medical image analysis, with a particular focus on eye diseases such as diabetic retinopathy. He develops AI methods that help clinicians diagnose and monitor disease more accurately, and that work reliably in real clinical settings. His work combines machine learning, computer vision and data science with close collaboration with healthcare professionals. His aim is to move AI tools from research into everyday use, where they can benefit patients and healthcare services. His research interests include:
- Artificial intelligence and deep learning for medical imaging
- Automated disease detection and diagnosis from medical images
- Medical image segmentation
- Predicting treatment outcomes using AI
- Trustworthy and reliable AI in healthcare
- Computer vision and image analysis
- Machine learning and data science
- AI applications in remote sensing and cyber security
He welcomes collaboration with clinicians, industry partners and other researchers, and enquiries from prospective PhD and MSc by Research students in these areas.
Recent publications
Zhang, Ting, Liu, Pengyi, Liu, Zhaoyinγ, Chen, Yingchun, Alfuhaid, Hisham, Al-Antary, Mohammad, Waqas, Muhammad and Hussain, Syed Mudassir (2026) Cross-domain few-shot infrared ship segmentation with class-specific adapters and SAM refinement. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. ISSN 1939-1404 (Print), 2151-1535 (Online) (doi:10.1109/JSTARS.2026.3704472)
Wang, Qingwang, Ouyang, Junlin, Shen, Tao, Gu, Yanfeng, Ullah, Sami, Al-Antary, Mohammad, Alasmary, Hisham, Waqas, Muhammad, Tang, Bo-Hui and Halim, Zahid (2026) Complementary information mining for redundancy and weakly aligned RGB-T semantic segmentation. IEEE Transactions on Geoscience and Remote Sensing, 64:5901414. pp. 1-14. ISSN 0196-2892 (Print), 1558-0644 (Online) (doi:10.1109/TGRS.2026.3654044)
A full list can be seen here.
Presentations and media activity
Recorded talk 'Diabetic retinopathy classification using deep learning', published by the BCS Open Source Specialist Group on YouTube. https://www.youtube.com/watch?v=5HxVsp8Wzgo
'E-Governance Best Tool To Improve Working Condition', 247 Ureports, 13 February 2020. News coverage of an executive training programme on e-governance for senior government officials from Kano State, Nigeria, organised by Lexington Hub Training Centre, London, which Dr Al-Antary co-facilitated. The programme addressed the role of information and communication technologies in public administration, education, healthcare and the civil service. https://247ureports.com/2020/02/e-governance-best-tool-to-improve-working-condition/
'Automated identification of diabetic retinopathy using pixel-based segmentation approach'. 2nd International Conference on Watermarking and Image Processing (ICWIP 2019), Marseille, France, September 2019.