Summary
Dr Ahmed Fetit is a Senior Teaching Fellow at the UKRI Centre for Doctoral Training in AI for Healthcare, where he is responsible for the teaching and research training provisions.
His research interests are in principled applications of machine/deep learning technologies to healthcare, particularly in medical imaging. He previously worked on several cross-disciplinary projects in collaboration with NHS Trusts across the UK.
Ahmed holds a PhD from the University of Warwick, where he studied applications of machine learning for the diagnosis and prognosis of tumours from clinical MRI data. He received his MSc and BEng (Hons) degrees from the University of Birmingham.
Publications
Journals
Richter L, Fetit A, 2022, Accurate segmentation of neonatal brain MRI with deep learning, Frontiers in Neuroinformatics, Vol:16, ISSN:1662-5196, Pages:1-18
Cabrera Y, Fetit A, 2022, Reducing CNN textural bias with k-space artifacts improves robustness, Ieee Access, Vol:10, ISSN:2169-3536, Pages:58431-58446
Conference
Chen H, Liu T, Hu S, et al., 2023, Web-based AI system for medical image segmentation, 27th Conference on Medical Image Understanding and Analysis (MIUA 2023), Springer
Xie Y, Fetit A, 2022, How effective is adversarial training of CNNs in medical image analysis?, 26th UK Conference on Medical Image Understanding and Analysis, Springer, Pages:443-457, ISSN:0302-9743
Chai S, Rueckert D, Fetit A, 2021, Reducing textural bias improves robustness of deep segmentation models, Annual Conference on Medical Image Understanding and Analysis (MIUA 2021), Springer Verlag, Pages:294-304, ISSN:0302-9743