Mohammad Yaqub

Associate Professor of Computer Vision

Research interests

Professor Yaqub’s research interest is in AI in healthcare applied to problems in medical image analysis (e.g, ultrasound, MRI and CT), radiomics and radiogenomics. He investigates and develops AI algorithms to solve real-world healthcare problems, explores fundamental machine learning methods such as continual learning and adversarial attacks and defense in the healthcare domain, and studies different healthcare challenges using natural language processing.

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Prior to joining MBZUAI, Professor Yaqub was a postdoctoral fellow for six years in the Institute of Biomedical Engineering at the University of Oxford where he worked on several medical imaging problems.

Professor Yaqub spent more than seven years in industry working as a consultant followed by a full position as vice president of engineering at Intelligent Ultrasound Limited, Oxfordshire, United Kingdom.

Professor Yaqub has also worked as a lecturer at Oxford EMI Training and the IT Learning Centre, University of Oxford. Professor Yaqub is a visiting fellow in the Nuffield Department of Clinical Neurosciences and the Oxford Acute Vascular Imaging Centre, University of Oxford, Oxford, United Kingdom.

  • Ph.D. in biomedical engineering from the University of Oxford, United Kingdom.
  • Patent filed 2022 - Title: Deep Learning Apparatus and Method for Segmentation and Survival Prediction for Head and Neck Tumors. USPTO application no.: 17849943
  • Best paper award at FAIR-MICCAI 2021 conference
  • Won a research competition in the Medical Image Computing and Computer Assisted Intervention (MICCAI 2021, the top conference in medical image analysis)
  • With colleagues from SEHA and Khalifa University, won the Grand Prize of Ericsson Together Apart 2021 hackathon UAE organized by Ericsson and under the patronage of Ministry of Economy
  • Honorary fellow at NDCN, University of Oxford (2018)
  • Publication Mohammad Yaqub

Yaqub has published more than 40 peer-reviewed articles in top conferences and journals such as IEEE TMI, Medical Image Analysis, MICCAI and Ultrasound in Medicine and Biology and co-edited two books entitled: Medical Imaging Understanding and Analysis, 2020 and 2021.

  • M Z Atwany, A H Sahyoun and M Yaqub, “Deep Learning Techniques For Diabetic Retinopathy Classification: A Survey,” in IEEE Access, 2022, doi: 10.1109/ACCESS.2022.3157632. [Impact factor: 3.367].
  • M Yaqub, B Kelly, JA Noble, AT Papageorghiou. The effect of maternal body mass index on fetal ultrasound image quality. Am J Obstet Gynecol. Published online April 2021.doi:10.1016/j.ajog.2021.04.248. [Impact factor: 6.502].
  • N Saeed, S E Hardan, K Abutalip, M Yaqub, Is it Possible to Predict MGMT Promoter Methylation from Brain Tumor MRI Scans using Deep Learning Models? In Medical Imaging with Deep Learning Conference, 2022.
  • I Sobirov, O Nazarov, H Alasmawi, M Yaqub, Automatic Segmentation of Head and Neck Tumor: How Powerful Transformers Are? In Medical Imaging with Deep Learning Conference, 2022.
  • N Saeed, R Al Majzoub, I Sobirov, M Yaqub, An Ensemble Approach for Patient Prognosis of Head and Neck Tumor Using Multimodal Data, HECTOK Challenge, MICCAI 2021.
  • S Srivastava, M Yaqub, K Nandakumar, Z Ge, D Mahapatra. Continual Domain Incremental Learning for Chest X-Ray Classification in Low-Resource Clinical Settings. In Domain Adaptation and Representation. Transfer, and Affordable Healthcare and AI for Resource.
  • Diverse Global Health (pp. 226–238), 2021. Springer.

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