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  Serverless Architecture for Medical Imaging Processing

   School of Computing

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  Dr Shufan Yang, Prof X Liu  Applications accepted all year round  Self-Funded PhD Students Only

About the Project

The emergence of new imaging modalities, such as Near-Infrared and Photoacoustic Spectroscopy, has demonstrated the potential for wide applications in neuroscience, clinical neurology, and personal healthcare. However, processing medical images is conventionally done offline, due to restraints on computational capacity. These limitations hinder wider applications, particularly for ambient healthcare. However, Function as a Service (Faas) and serverless computing provide a tantalising alternative, utilizing lightweight virtual machine containers which widen the spectrum of applications that can be deployed in a cloud environment. This serverless architecture provides accessible distributed computational power outside of large cluster environments. The aim of this PhD project is to develop a new generation of serverless architecture for real-time medical imaging processing. This intelligent processing platform will be used for multi-modality imaging processing for diagnosis and patient healthcare. The PhD student will gain substantial knowledge in fields such as cloud computing, service-oriented architecture, AI hardware, optical imaging, and imaging processing, and will gain various machine learning and data analytical skills.

Academic Qualifications:

A first degree in a relevant scientific discipline, such as computer science, engineering, mathematics, physics, or medicine. Desirable skills include mathematics, statistics, machine learning, computer vision, and software engineering.

English Language requirement

IELTS score must be at least 6.0. Other equivalent qualifications will be accepted. Full details of the University’s policy are available online.

Essential attributes:

Edinburgh Napier University is committed to promoting equality and diversity in our staff and student community

Computer Science (8) Mathematics (25)

Funding Notes

Candidates who have been offered a place for PhD study at the School of Computing may be considered for funding by the School.


Nussbaumer, N., & Liu, X. (2013, July). Cloud migration for SMEs in a service oriented approach. In 2013 IEEE 37th Annual Computer Software and Applications Conference Workshops (pp. 457-462). IEEE.
Yang, S., and Yu, Z. (2019) A highly integrated hardware-software co-design and co-verification platform. IEEE Design and Test. (doi:10.1109/MDAT.2018.2841029)
Yang, S., Cox, B. F., Lemke C., Newton, I. P., Näthke, I., and Cochran S., (2020), A Learning Based Microultrasound System for the Detection of Inflammation of the Gastrointestinal Tract , IEEE Transaction Medical Imaging, DOI: 10.1109/TMI.2020.3021560
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