Dr Neda Azarmehr
BSc, MSc, PhD (University of Lincoln), FHEA (University of ºù«Ӱҵ)
Information School
Lecturer in Data Science and AI
Full contact details
Information School
The Wave
2 Whitham Road
ºù«Ӱҵ
S10 2AH
- Profile
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I joined the Information School in 2025 and hold a PhD in Computer Science with a focus on Artificial Intelligence from the University of Lincoln (2021), completed in collaboration with Imperial College London. After PhD completion, I worked as a Postdoctoral Research Fellow in the NEOPATH Research
Group at the University of ºù«Ӱҵ School of Dentistry, where I developed advanced AI algorithms for Cancer Research UK-funded projects in collaboration with the University of Warwick.
Prior to this, I was a Lecturer in Computer Science and the Course Director of the MSc Artificial Intelligence program at the University of West London, within the School of Computing and Engineering. I am endorsed as an emerging leader by UKRI and am a Fellow of the Higher Education Academy. My research focuses on a wide spectrum of AI disciplines, with a particular emphasis on machine learning, deep learning and computer vision techniques, in healthcare domains such as
medical imaging and computer-aided diagnosis.
- Research interests
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My current research focuses on developing computational models using advanced computer vision
and multimodal artificial intelligence (AI) to support clinicians in decision-making. My expertise are
research and development experience in medical imaging, processing, deep learning computer
vision, and computer aided diagnosis techniques. Examples of my research include automated echocardiography view detection with a focus on lightweight deep neural network, left ventricle segmentation, speckle tracking using echocardiographic images, automated detection of colonic polyps, and the segmentation and quantification of digital pathology images with a focus on head and neck cancer. I am also interested in the domain of trustworthy AI, involving issues such as bias, fairness, interpretability, and ethical considerations in algorithm development and inference. These efforts aim to ensure that AI solutions are not only technically robust but also ethically sound and socially responsible, paving the way for equitable and trustworthy AI applications.I am interested in supervising PhD students who are passionate about advancing AI research with real-world impact, particularly in healthcare applications. My primary areas of interest include: Developing innovative AI algorithms for medical imaging and diagnostics, addressing critical tasks such as disease detection, segmentation, and prognosis prediction across various imaging modalities. A significant aspect of this work involves integrating imaging data with clinical and
genomic information to create multimodal AI systems that enhance diagnostic precision, reveal complex patterns, and pave the way for personalised treatments. Additionally, I am exploring the potential of generative models, such as diffusion models and GANs, to address data scarcity. Another area of interest lies in designing lightweight AI models that can be deployed on portable imaging devices, such as handheld ultrasound systems. These models are aimed at expanding healthcare access in resource-constrained environments. Also, I am interested the development of AI algorithms to enhance real-time, image-guided interventions, such as surgeries and biopsies, with a focus on integrating these algorithms into robotic systems for precision and automation in minimally invasive procedures. Equally important is trustworthy and ethical AI, so Investigating methods to identify and mitigate bias in datasets and algorithms, ensuring fairness and equitable outcomes across diverse populations. Developing explainable AI frameworks and tools to enhance trust and transparency.
- Publications
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Journal articles
- . Computer Methods and Programs in Biomedicine, 248, 108111-108111.
- . Computers in Biology and Medicine, 171, 108192-108192.
- . The Journal of Pathology, 260(4), 431-442.
- . Journal of Medical Imaging, 8(3).
- . Computers in Biology and Medicine, 133.
- . Medical and Biological Engineering and Computing, 58(6), 1309-1323.
- Automated segmentation of left ventricle in 2D echocardiography using deep learning. MIDL 2019 : Medical Imaging with Deep Learning, extended abstracts.
Chapters
- , Lecture Notes in Computer Science (pp. 124-132). Springer Nature Switzerland
- , Lecture Notes in Computer Science (pp. 394-402). Springer Nature Switzerland
- , Lecture Notes in Computer Science (pp. 283-291). Springer Nature Switzerland
- , Lecture Notes in Computer Science (pp. 357-370). Springer International Publishing
Conference proceedings papers
- Echocardiographic phase detection using neural networks. MIDL 2021 : Medical Imaging with Deep Learning, Proceedings. Lübeck, Germany (online), 7 July 2021 - 7 July 2021.
- Automated assessment of image quality in 2D echocardiography using deep learning. International Conference on Radiology, Medical Imaging and Radiation Oncology (ICRMIRO), Proceedings (pp 2160-2165). Paris, France, 25 June 2020 - 25 June 2020.
- . Medical Image Understanding and Analysis(1065) (pp 497-504). Liverpool, UK, 24 July 2019 - 24 July 2019.
Preprints
- , Cold Spring Harbor Laboratory.
- , Research Square Platform LLC.
- . Computer Methods and Programs in Biomedicine, 248, 108111-108111.
- Grants
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£11,712 2022 - Fellowship exchange program, ºù«Ӱҵ, Yale University - RadioPathomic Integrated Artificial Intelligence System to Predict Salivary Gland Cancers project.
£1500 2022 - Insigneo Summer Research Programme, Deep learning-based methods for automated radiological detection of jaw lesions project. Best Poster Award in Women in Conference on Medical Image Understanding and Analysis, WiMIUA, MIUA 2022, (3rd place), Issued by WiMIUA, MIUA 2022 · Jul 2022
- Teaching activities
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MSc Data Science Program
INF6028- Data Mining
INF6032- Big Data Analytics
- Professional activities and memberships
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Fellow of Higher Education Academy (FHEA)
Member of IEEE
Member of ESDIP (European Society of Digital and Integrative Pathology)
I regularly review manuscripts for the journals and conferences:
Computer In Biology and Medicine
PLOS ONE
Medical Image Understanding and Analysis (MIUA) 2022 conference