Bartek Papiez
PhD, MSc
Associate Professor
- Group Lead for Medical Image Analysis and Machine Learning
At the Big Data Institute, I have established an independent research group that focuses on medical imaging and machine learning. I am proud to have initiated several new, multidisciplinary research projects that integrate imaging and non-imaging modalities, driving the development of innovative image analysis and machine learning algorithms. Notably, my research projects encompass both the theoretical foundations of AI/ML algorithms (such as image quality, image segmentation, or image registration), and applied AI/ML for longitudinal disease monitoring (using imaging, patient records, and Natural Language Processing), identification of disease therapeutic targets (using imaging & genetic data integration), and more recently, multimodal cancer imaging & radiogenomics.
Education. Bartek graduated in Electrical Engineering from the AGH University of Science and Technology in Kraków (Poland) in 2009. He completed a PhD at the University of Central Lancashire (UK) in 2012.
Research. Bartek joined the Biomedical Image Analysis Laboratory at the University of Oxford and between 2012 and 2017, he worked as a post-doctoral research fellow at the Oxford Cancer Imaging Centre focusing on cancer image analysis. In 2013, he was awarded a prestigious Young Scientist Award by the Medical Image Computing and Computer Assisted Intervention Society. In 2015, he was elected to an EPA Cephalosporin Junior Research Fellow at Linacre College. In 2018, he was awarded Rutherford Fund Fellowship at Health Data Research UK at the Big Data Institute in Oxford, which extended in Senior Fellowship in Population Health (from 2021).
Teaching. Bartek was a retained lecturer in Engineering Science at Exeter College (2018-20). Currently, I serve as a stipendiary lecturer at St Peter's College, and Lady Margaret Hall. Bartek provides also lectures/workshop on Artificial Intelligence and Machine Learning (AI/ML) for Healthcare, Biomedical Image Analysis, Medical Imaging for various departments across the university and the external parties.
Research project to discuss? Expertise needed? Training or lectures needed? Contact by email.
Recent publications
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Accurate volume alignment of arbitrarily oriented tibiae based on a mutual attention network for osteoarthritis analysis.
Journal article
Zheng J-Q. et al, (2023), Comput Med Imaging Graph, 106
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Learning to restore multiple image degradations simultaneously
Journal article
Zhang L. et al, (2023), Pattern Recognition, 136
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Data from Functional Parameters Derived from Magnetic Resonance Imaging Reflect Vascular Morphology in Preclinical Tumors and in Human Liver Metastases
Other
Kannan P. et al, (2023)
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Data from Functional Parameters Derived from Magnetic Resonance Imaging Reflect Vascular Morphology in Preclinical Tumors and in Human Liver Metastases
Other
Kannan P. et al, (2023)
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Supplementary Information from Functional Parameters Derived from Magnetic Resonance Imaging Reflect Vascular Morphology in Preclinical Tumors and in Human Liver Metastases
Other
Kannan P. et al, (2023)