Dr Neda Azarmehr
BSc, MSc, PhD
School of Clinical Dentistry
Postdoctoral Research Fellow
Full contact details
School of Clinical Dentistry
19 Claremont Crescent
Neda obtained her PhD in Computer Science/Artificial Intelligence from University of Lincoln. Her PhD project was collaboration with Imperial College London to develop automated models using deep learning, computer vision algorithm, and medical image analysis to assess the left ventricle function which enables physicians to analyse cardiac echo images more precisely.
She has completed undergraduate studies in Health Information Technology, following a MSc degree in Big Data Analytics. She has a strong interest in teaching and research in the field of application of Artificial Intelligence in medical application. Before joining The University of Sheffield, Neda worked as an associate lecturer in the field of Computer Science at different UK higher education institutions.
- Research interests
Neda's research interests are mainly centred on algorithm development, computer vision, machine learning, deep learning, medical image analysis.
Neda’s current research focuses on developing automated models using Artificial Intelligence for early oral cancer detection (Supervisor: Dr Ali Khurram) on the multidisciplinary collaboration with CR_UK ANTICIPATE study with Professor Rajpoot, University of Warwick.
She has published research articles in national and international journals and conferences, including a book. Neda is a reviewer for several peer-reviewed international journals.
- Neural architecture search of echocardiography view classifiers. Journal of Medical Imaging, 8(3). View this article in WRRO
- Multibeat echocardiographic phase detection using deep neural networks. Computers in Biology and Medicine, 133. View this article in WRRO
- An optimisation-based iterative approach for speckle tracking echocardiography. Medical and Biological Engineering and Computing, 58(6), 1309-1323. View this article in WRRO
- View this article in WRRO Automated Segmentation of Left Ventricle in 2D echocardiography using deep learning. MIDL/2019/ExtendedAbstract/Sye8klvmcN; MyUni-UID.
- A Neural Architecture Search Based Framework for Segmentation of Epithelium, Nuclei and Oral Epithelial Dysplasia Grading, Medical Image Understanding and Analysis (pp. 357-370). Springer International Publishing
Conference proceedings papers
- View this article in WRRO Echocardiographic Phase Detection Using Neural Networks. Echocardiographic Phase Detection Using Neural Networks
- View this article in WRRO Automated Assessment of Image Quality in 2D Echocardiography Using Deep Learning. Automated Assessment of Image Quality in 2D Echocardiography Using Deep Learning
- Segmentation of Left Ventricle in 2D Echocardiography Using Deep Learning (pp 497-504) View this article in WRRO
- Using Artificial Intelligence for Analysis of Histological and Morphological Diversity in Salivary Gland Tumors, Research Square Platform LLC.
Funded by CR UK
- Teaching interests
Artificial Intelligence in medical domain, Machine Learning.
- Professional activities and memberships
Member of Insigneo Institute for in silico Medicine.