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.
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
Xie Y, Fetit A, How effective is adversarial training of CNNs in medical image analysis?, 26th UK Conference on Medical Image Understanding and Analysis, Springer, 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
et al., 2020, A deep learning approach to segmentation of the developing cortex in fetal brain MRI with minimal manual labeling., Proceedings of the Third Conference on Medical Imaging with Deep Learning, PMLR, Pages:241-261